foreign object debris material recognition based on

RESEARCH Open Access

Foreign object debris material recognitionbased on convolutional neural networksHaoyu Xu12 Zhenqi Han13 Songlin Feng1 Han Zhou1 and Yuchun Fang3

Abstract

The material attributes of foreign object debris (FOD) are the most crucial factors to understand the level of damagesustained by an aircraft However the prevalent FOD detection systems lack an effective method for automatic materialrecognition This paper proposes a novel FOD material recognition approach based on both transfer learning and amainstream deep convolutional neural network (D-CNN) model To this end we create an FOD image datasetconsisting of images from the runways of Shanghai Hongqiao International Airport and the campus of ourresearch institute We optimize the architecture of the D-CNN by considering the characteristics of the materialdistribution of the FOD The results show that the proposed approach can improve the accuracy of materialrecognition by 396 over the state-of-the-art method The work here will help enhance the intelligence capability offuture FOD detection systems and encourage other practical applications of material recognition technology

Keywords Foreign object debris Material recognition Deep learning Deep convolutional neural networksTransfer learning

1 IntroductionForeign object debris (FOD) refers to any object locatedin and around an airport (especially on the runway andthe taxiway) that can damage the aircraft or harm air-carrier personnel [1] Typical examples of FOD includetwisted metal strips components detached from aircraftor vehicles concrete chunks from the runway and plasticproducts FOD poses a safety risk to an aircraft and asignificant economic loss to airlines The crash of AirFrance Flight 4590 that killed 113 personnel in 2000 wascaused by a twisted metal strip [2] as shown in Fig 1Moreover the direct economic loss due to FOD damage isconservatively estimated to be 3 ~ 4 billion USD per year [3]To reduce or eliminate FOD damages certain companies

have developed FOD detection systems such as the Tarsiersystem by QinetiQ FODetect by Xsight and iFerret byStratech [4] All these systems use a camera to take aphotograph of suspicious FOD and then the photographsare verified by human experts These systems have beencommercially deployed in a few airports but have not

achieved large-scale global usage One main reason for thislow-level deployment is that the final FOD verification steprelies exclusively on recognition by a human expert whichhas two disadvantages The first disadvantage is thatreliable verification requires a capable and experiencedofficial which incurs additional cost for the airport authorityFor example the Vancouver Airport filled this position withan employee from its FOD vendor The second disadvantageis that peoplersquos recognition capability is not completelytrustworthy because they are inevitably fatigued from timeto timeHan et al [5 6] worked on FOD object recognition

using a support vector machine (SVM) and randomforest FOD object recognition is to identify what theFOD is Unfortunately the exact nature of FOD is variedbecause FOD can be composed of any object any colorand any size Over 60 of the FOD items are made ofmetal Therefore recognition of the FOD materialconstitution has much greater practical significance thanobject recognitionMaterial recognition is a fundamental problem in

computer vision In contrast with the several decades ofobject recognition research material recognition hasonly begun receiving attention in recent years It is aflourishing and challenging field The approaches to

Correspondence hanzqsariaccn1Shanghai Advanced Research Institute Chinese Academy of SciencesShanghai 201210 China3School of Computer Engineering and Sciences Shanghai UniversityShanghai 200444 ChinaFull list of author information is available at the end of the article

EURASIP Journal on Imageand Video Processing

copy The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 40International License (httpcreativecommonsorglicensesby40) which permits unrestricted use distribution andreproduction in any medium provided you give appropriate credit to the original author(s) and the source provide a link tothe Creative Commons license and indicate if changes were made

Xu et al EURASIP Journal on Image and Video Processing (2018) 201821 httpsdoiorg101186s13640-018-0261-2

material recognition can be broadly categorized as hand-crafted or automatic feature extraction Hand-craftedapproaches can be further divided into surface reflectance[7ndash11] 3D texture [12ndash19] and feature fusion [20ndash22]approaches Automatic feature extraction approaches referto those that involve acquiring image features using a deepconvolutional neural network (D-CNN) [23ndash26]There are some correlations between the surface

reflectance properties and the categories of materialsFor example wooden surfaces tend to be brownwhereas metallic ones tend to be shiny However differentclasses of materials may exhibit similar reflectance proper-ties such as the translucence of plastic wax and glassConsequently understanding an objectrsquos reflectanceproperties is insufficient to determine its material consti-tution Similarly different materials may have the same3D texture pattern as shown in Fig 2 To overcome thesechallenges researchers have attempted to combinedifferent features or have attempted automatic featureextraction to perform material recognition tasks Someremarkable results were obtained on some specific datasetsin certain studiesHowever past research results remain inadequate to

meet the demands of FOD material recognition Firstthere is no specific FOD dataset for the task because ofthe unique airport environment Although Bell et al [25]used more than 300 million image patches for trainingthe images were acquired mainly in indoor environmentswhere light conditions are quite different from the FODemergence locations The results were hence quite poor

when these 300 million image patches were used fortraining while FOD images were used for testing (pleaserefer to the ldquoSection 4rdquo for details) Second a high-recognition ratio is necessary for metal recognitionMetallic objects are far more harmful than othermaterials Meanwhile 60 of FOD is constituted by metal[1] However according to prior results [19 21] therecognition rate was quite low for metallic objectsThis paper proposes a novel FOD material recognition

approach based on transfer learning and a mainstreamdeep convolutional neural network (D-CNN) modelThis paper describes an FOD image dataset consisting ofimages taken on the runways of Shanghai HongqiaoInternational Airport and the campus of our researchinstitute The dataset consisted of 3470 images dividedinto three categories by material metal concrete andplastic The proposed approach is optimized torecognize metal because of the high risk that is due toits high-damage level to aircrafts and its high occurrencefrequency in airportsThis research will help improve the intelligence

capability the ease of using and the user experience ofFOD detection systems It will also encourage moreapplications of material recognition systems especiallyin security and manufacturing such as construction sitemanagement [27 28]The rest of this paper is organized as follows Section

2 introduces related work and our approach is describedin Section 3 Section 4 presents a discussion of theexperiment results and Section 5 summarizes ourconclusion and plan for future work

2 Related workMaterial recognition a fundamental problem in computervision has a wide range of applications For example anautonomous vehicle or a mobile robot can make decisionson whether a forthcoming terrain is asphalt gravel ice orgrass A cleaning robot can distinguish among wood tileor carpet The approaches to material recognition arebroadly divided into two categories according to featureextraction methods hand-crafted features and automaticfeatures Hand-crafted approaches can be further dividedinto surface reflectance-based 3D texture-based andfeature fusion-based approaches Automatic featureextraction approaches refer to those acquiring imagefeatures through a D-CNNThe most popular formalization for model surface

reflectance is the bidirectional reflectance distributionfunction (BRDF) This function defines the amount oflight reflected at a given point on a surface for anycombination of incidence and reflection angles [21] TheBRDF has a parametric type [29 30] and an empiricaltype [7ndash9 11] Parametric BRDF models cannot acquirea broad set of real-world reflectance properties In

Fig 1 The twisted metal strip that caused Air France Flight 4590 to crash

Fig 2 Surfaces with similar textures may be composed of differentmaterials These objects are made of fabric plastic and paper fromleft to right [21]

Xu et al EURASIP Journal on Image and Video Processing (2018) 201821 Page 2 of 10

contrast empirical BRDF models always require priorknowledge such as illumination conditions geometryand surface material properties Such prior knowledgecannot be expected to be available for real-world imagesZhang et al [10] introduced an empirical model basedon a reflectance disk and reflectance hashing Thereflectance disk a measurement of the surface propertywas built using a customized camera apparatus Gaussianlow-pass filters Laplacian filters and gradient filters wereapplied to the reflectance disk Textons referring tofundamental micro-structures in natural images werecomputed by k-means clustering on the output of thefilter banks Following this approach texton boosting andreflectance hashing were employed for feature selectionand image classification This approach is not feasible forreal-world images as reflectance disks are generated by acustomized apparatus in a laboratory environmentMoreover different surface materials may exhibit similarreflectance phenomena for example plastic glass andwax are translucent Therefore fulfilling the goal ofmaterial recognition only by using surface reflectanceproperties appears to be difficultThree-dimensional texture refers to surface roughness

that can be resolved by the human eye or a camera Suchtexture-based approaches follow the feature extraction-and-classification routine Various researchers used anumber of descriptors to extract the local features of animage For example some studies [12ndash17] used themaximum response one study [18] used sorted randomprojections and another study [19] applied a kerneldescriptor for this purpose These feature vectors werethen fed into a classifier usually SVM latent Dirichletallocation (LDA) or nearest neighbor These approacheswere designed to obtain salient results on CUReT [15ndash17]ETHTIPS [12ndash14] and FMD [20] However these datasetsare inappropriate for FOD material recognition tasks Theimages from CUReT and ETHTIPS datasets were capturedusing a customized apparatus in an ideal laboratory envir-onment These images not only had different appearancewith real-world images but also were unobtainable in dailylife The FMD dataset is composed of real-world imagesfrom the website Flickr However the FMD dataset suffersthree downsides with regard to FOD recognition (1) Fewsamples are FOD alike (2) The photos are barely takenoutdoors (3) There is a lack of intentional collection ofimages of metal concrete and plastic materialsSharan et al [20ndash22] combined reflectance and 3D

texture into new fused features as input to an LDA or anSVM classifier They chose four groups of features namelycolor and texture (eg Color Jet and SIFT) micro-texture(eg Micro-Jet and Micro-SIFT) shape (eg curvature) andreflectance (Edge-Slice and Edge-Ribbon) As the previouswork this research was also performed on an FMD datasetthat made it unfeasible for FOD material recognition tasks

Since Hintonrsquos monumental work [31] in 2006 deeplearning has received considerable attention in bothacademia and industry because of its superior performanceover other machine learning methods He et al [32] used a152-layer D-CNN to obtain a 357 error rate on theILSVRS2015 dataset The result was better than the errorof 51 incurred by humans [33] Researchers haveattempted to apply D-CNNs to automatically extract imagefeatures to achieve material recognition Cimpoi et al [2324] proposed the Fisher-vector CNN via amelioration ofthe pooling layer in the D-CNN They reported a consider-able amount of improvement over the work by Sharen etal [21 22] Bell et al [25] proposed a new dataset theMaterial-in-context Database (MINC) for material recogni-tion based on Imagenet [34] They achieved an impressiverecognition accuracy of 852 by utilizing Alexnet [35] andGoogLeNet models [36] Zhang et al [26] assumed that thefeatures for object recognition could be helpful for materialrecognition to some extent and integrated features learnedfrom the ILSVRC2012 [33] and the MINC [25] The resultswere state-of-the-art as expected However the MINCdataset was built using images taken from indoor environ-ments which are unsuitable for FOD material recognition

3 Method31 Dataset constructionThe ColumbiandashUtrecht Reflectance and Texture Database(CUReT) [15 17] the KTH-TIPS [13] the Flickr MaterialDatabase (FMD) [22] and the Material-in-context Database(MINC) [25] are open datasets for material recognitionCUReT consists of 61 textures imaged under 205 diverseillumination and angle conditions KTH-TIPS has 11material categories each category with four samples imagedunder various conditions Images in the FMD dataset arefrom the Flicker website This dataset has 1000 images of10 material categories MINC has approximately 300million image patches tailored from ImageNet As stated inSection 2 these four datasets are improper for FODmaterial recognitionWe choose metal plastic and concrete as three typical

FOD materials to construct the dataset According toFAArsquos AC 1505220-24 [1] these materials appear mostfrequently on runways and taxiways Furthermoremetallic FOD constitutes approximately 60 of all FODMetal and plastic may exhibit similarly intense reflectancephenomena under strong light in outdoor environmentsTo complicate things further these images are taken onrunways or taxiways which are made of concrete Theconcrete background of metal or plastic images poses atremendous challenge to distinguishing these twomaterials from the background It is similar with detectingUyghur language text from complex backgrounds [37]Therefore careful treatment is imperative to recognizemetal plastic and concrete correctly


We constructed the FOD dataset using two approachesThe first approach involved taking the images in thebackup runway of Shanghai Hongqiao InternationalAirport It appears to be impossible for us to collectimages at a large scale because of the airportrsquos strict entryrequirements Hundreds of images taken from HongqiaoAirport were used as test data The other approach toconstruct the dataset was to collect images at the campusof our institute We chose a campus road made ofconcrete to emulate as closely as possible the runwayenvironment We used a HikVision DS-2df8523iw-acamera with a maximum focal distance of 135 mm and amaximum resolution of 1280 times 768 The camera wasmounted two meters from the ground and the FOD waslocated approximately five meters from the camera Thissetting was proportionally in accordance with the typicalsetup of a FOD detection system The metallic FOD usedincludes wrenches screws nuts metal strips rusty nailsand iron sheets of various shapes and sizes For plasticFOD diverse shapes and sizes of polyethylene plasticpipes bags and straws were chosen We chose differentshapes and sizes of concrete blocks stone blocks andpebbles as samples of concrete Images of each materialcategory were taken from 9 am to 5 pm on separatedays to ensure different illumination circumstances Wealso captured images from different angles Figure 3 showsthe typical image samples for the metallic plastic andconcrete material categories from top to bottom Theoriginal image sample was divided into N timesN size patchesby hand The patches were further resized to 256 times 256 forconvenient processing in CaffeTable 1 summarizes the statistics of the FOD dataset

The images taken in indoor environments were only usedto compare our system with the prevalent D-CNN modelsto gauge their different performances for indoor andoutdoor objects These images were not used in either thetraining or the testing of our proposed D-CNN modelThe FOD dataset introduced in this paper was different

in three aspects from previous datasets First there was asignificant extent of intra-class variations for each materialcategory Images belonging to the same material category

usually had completely different shapes or even identitiesSecond all images had concrete as the backgroundemulating the circumstances in airports Third all imageswere captured in outdoor environments

32 Choice of the D-CNN modelA D-CNN an extremely efficient and automatic feature-learning approach transforms an original input to ahigher-level and more abstract representation usingnon-linear models A D-CNN is composed of multipleconvolution layers pooling layers fully connected layersand classification layers The network parameters areoptimized through the back-propagation algorithmD-CNNs have a broad set of applications in image

classification object recognition and detection Glasssix[38] trained a CNN with an improved ResNet34 layerand obtained 9983 accuracy on the famous LFW facerecognition database Considering the scale contextsampling and deep combined convolutional networksthe BDTA team won the championship of theILSVRC2017 object detection task The Subbmission4model provided by BDTA can detect 85 object categoriesand achieved a 073 mean average precision on DET task1a (object detection with provided training data) [39]Chen et al provided an effective CNN named Dual PathNetworks for object localization and object classificationwhich obtained a 62 localization error rate and a 34classification error rate on the ILSVRC2017 objectlocalization task [40] Yan et al provided a supervisedhash coding with deep neural network for environmentperception of intelligent vehicles and the proposedmethod can obviously improve the search accuracy [41]AlexNet [35] GoogLeNet [36] and VGG-16 [42] are

widely established and open D-CNN models They arede-facto candidate base models for researchers AlexNetis composed of five convolutional layers and three fullyconnected layers VGG-16 is composed of 13 convolu-tional layers and three fully connected layers andGoogLeNet is composed of 21 convolutional layers andone fully connected layer The main size of the convolu-tional kernel for AlexNet and VGG-16 is three by threewhereas that of GoogLeNet is the inception modulewhich is a two-layer convolutional network BothAlexNet and VGG-16 use the maximum poolingmechanism By contrast GoogLeNet applies both the

Fig 3 Typical image samples of the FOD dataset

Table 1 Statistics of the FOD dataset

Indoor Campus road training Airport runway and campusroad testing

Metal 30 1000 105

Plastic 0 1000 235

Concrete 0 1000 100


maximum pooling and the average pooling schemes Wedescribe three D-CNN models in more detail in Table 2The following choices were made with the help ofexperimental results AlexNet was chosen as the basemode in our approach as it yields the best performancefor metallic FOD Regardless of the model employedmetallic FOD is easily confused with plastic or concreteFOD This observation verifies our choice of materialcategories in addition to the reason that they occurmost frequently on runways or taxiways Please refer toSection 4 for detailed resultsWith the increase of network depth the recognition

accuracies of VGG and GoogLeNet on outdoor metalimages shown in Fig 8 are reduced We conjecturethat ResNet [32] may have a low accuracy rate forFOD images from an outdoor environment Thus wedid not perform experiments using ResNet on theFOD material dataset

33 Transfer learningThe technique of transfer learning is applied in thispaper to avoid the overfitting problem Transfer learningis literally defined as the transfer of knowledge learnedin one domain to another domain The technique isespecially useful for the D-CNN models because of theirhigh demand in terms of the huge amount of human-labeled training data [43 44] Without sufficient trainingdata the D-CNN models tend to be over-fitted It wouldbe truly favorable to reduce the need and effort tocollect clean and label a large amount of data with thehelp of transfer learningIn this paper the parameters of the improved AlexNet

model are initialized by those trained from MINC Thismodel continues to be trained by fine-tuning the weightsof all layers based on the FOD dataset discussed inSection 4 It is observed that earlier layersrsquo features of aD-CNN entail more generic features (eg edge detectorsor color blob detectors) that are reusable for many tasks[45] In addition later layers of the D-CNN containdetails more specific in the original dataset eg MINCThe weights of later layers should be optimized morethan the ones of earlier layers with the help of the newdataset eg the FOD dataset Therefore the dedicatedchoice of weightsrsquo initialization is equivalent to shorteningthe distance from the starting point to the optimumwhich helps avoid the overfitting problemTransfer learning has achieved a wide range of applica-

tions in many tasks In the recognition task Reyes et alpre-trained a CNN using 18 million images and used afine-tuning strategy to transfer learned recognitioncapabilities from the general domains to the specificchallenge of the Plant Identification task [46] Bell et altrained all of their CNNs for material recognition byfine-tuning the network starting from the weightsobtained on 12 million images from ImageNet (ILSVRC2012) [25] In object detection OverFeat [47] thewinner of the location task of ILSVRC2013 also usedtransfer learning Google DeepMind used transfer learningto solve complex sequences of tasks [48]

34 Improved D-CNN based on AlexNetInspired by transfer learning an improved D-CNN basedon AlexNet is described in this section The improved

Fig 4 Image convolution

Table 2 Detailed descriptions of AlexNet VGG-16 and GoogLeNet

Alexnet VGG16 GoogLeNet ImprovedAlexNet

Input (RGB image) 227227 224224 224224 227227

Convolution (kernelsizestride)

11114 331 772 11114

331

Max pooling 332 222 332 332


551 331 331 551

331

Max pooling 332 222 332 332


331 331 Inception(3a) 331

331 331 Inception(3b) 331

331 111 331

Max pooling 332 222 332 332


331 Inception(4a)

331 Inception(4b)

111 Inception(4c)

Inception(4d)

Inception(4e)

Max pooling 222 332


331 Inception(5a)

331 Inception(5b)

111

Pooling Max pool222

Average pool771

Linear FC-4096 FC-4096 FC-1000 FC-4096

FC-4096 FC-4096 FC-4096

FC-1000 FC-1000 FC-1000

FC-3

Output Softmax Softmax Softmax Softmax


D-CNN model has an additional fully connected layerappended to the model as the last layer It shares thefirst eight layers with AlexNet hence the model consistsof five convolution layers and four fully connected layersThe ninth layer has three neuronal nodes indicating thatthe network has three material tag outputs We usesoftmax loss as the classifier The detailed networkstructure is shown in Table 2 The experiments wereconducted on the Caffe framework with Nvidia Tesla K20GPU card Caffe [49] is a highly effective framework fordeep learning such as Yan et alrsquos framework [50ndash53] forHEVC coding unit Using the FOD training dataset wefine-tuned the improved D-CNN based on pre-trainingthe weights in MINC Our implementation for FOD ma-terial recognition follows the practice in Krizhevskyrsquos andHersquos papers[32 35] During training the inputs to the im-proved D-CNN were fixed-size 224 times 224 RGB imagesThe batch was set to 256 the momentum was set to09 the weight decay (the L2 penalty multiplier) wasset to 05 and the learning rate was set to 0001 Intotal the learning rate was decreased three times andthe learning was stopped after 20-K iterations TheFOD testing dataset was used for the FOD materialrecognition test after the fine-tuning stage All of ourexperiment base above hyperparameters achievedstate-of-the-art results

The convolution layer learns input features throughthe convolution of the kernel and the input vectors asshown in Fig 4 The convolution function is given byEq (1)

hki j frac14 RELUethWk XTHORNi j thorn bk

eth1THORN

In Eq (1) W is the weight of the convolution kerneland k indicates the number of convolution kernels hkij is

the output of the convolution kernel k in the outputlayer (i j) X is the input of the convolution layer and bis the offset RELU is an activation function f(x) = max(0x) which is zero when x lt 0 and is linear with slope 1when x gt 0 as shown in Fig 5 Compared to the sigmoidand tanh functions RELU can greatly accelerate theconvergence of stochastic gradient descent [35]The pooling layer is a down-sampling process that

reduces the computational complexity and retains therotational invariance of imagesmdashsee Fig 6 Mean poolingand max pooling are usually used in the pooling layer andinvolve averaging the image area and choosing themaximum value respectivelyIn the output layer of the network the softmax classi-

fier is applied to classify samples The class tag of the

Fig 5 RELU function

Fig 6 Pooling

Fig 7 Samples from the FOD verification dataset

Fig 8 Indoor and outdoor image testing results for GoogLeNet AlexNetand VGG-16 for metal


maximum possibility is chosen as the output result Theprocess of the output layer can be described as Eq (2)

y frac14 arg maxi

eziPNjfrac141e

z j

eth2THORN

where z is the activation value of last layer neurons Theactivation value of neuron i is zi N is the number ofcategories and the number of last layer neurons y is theprediction tag For example the FOD dataset has threeclass of images so N = 3 in which 1 denotes metal 2denotes plastic and 3 denotes concrete

4 Experimental results and discussion41 Improved D-CNN based on AlexNetWe chose the base model from AlexNet VGG-16 andGoogLeNet All of these models were trained on theMINC dataset The model with the best recognitionaccuracy for metal was chosen as the base transfer learningmodel The FOD verification dataset consisted of 24 indoorimages (indoor images were included for performancecomparison although there was no indoor case for FODdetection) and 76 outdoor images as shown in Fig 7 Thefirst row is the indoor images of the metal items and thesecond row consists of the outdoor imagesFigure 8 shows the accuracy of FOD material recognition

for the three models in the indoor and outdoor cases Allthree convolution networks trained by the MINC datasetrecorded an approximately 80 accuracy for indoor

images but yielded poor performance for the outdoor caseAlexNet had an accuracy of 13 VGG-16 had an accuracyof 5 and GoogLeNetrsquos accuracy was close to 0 Theunsatisfactory performance of the above three D-CNNswas mainly because the MINC dataset was built usingimages from Houzz which collects interior decorationimages These pictures are usually taken under softillumination and without strong reflectance By contrastFOD on runways or taxiways often experiences strongillumination heavy rain and dense fog It is obvious thatthese three D-CNN models trained on the MINC datasetare not directly applicable to FOD material recognitionAlexNet has the best metallic material recognitionTable 3 displays the material classification results for

metal for GoogLeNet AlexNet and VGG-16 trained onthe MINC dataset The results show that GoogLeNetmisclassified most metals as plastic or stone AlexNetmisclassified metal as stone water paper and plasticand VGG-16 misclassified metal as plastic and stoneThis set of results show that metallic FOD tends to beeasily misclassified as plastic and concrete This observationjustifies our choice of material categories

42 Results of the improved modelIn this section we compared the performance of thethree D-CNN models The first was AlexNet withparameters trained by MINC abbreviated as AM Thesecond was AlexNet with parameters trained by boththe MINC and FOD datasetsmdashthis model was called

Table 3 Outdoor metal image test results for AlexNet VGG-16 and GoogLeNet trained on the MINC dataset

Carpet Ceramic Foliage Leather Metal Paper Plastic Stone Water

AlexNet 0053 0013 0 0026 0132 0132 0092 0289 0263

VGG-16 0 0 0 0 0053 0053 0447 0355 0092

GoogLeNet 0 0 0013 0 0 0066 0605 0276 0039

Fig 9 FOD material recognition results


AMF The third model was the improved model shownin Table 2 with parameters trained by the MINC andFOD datasets abbreviated as IAMF All experimentswere conducted within the framework of CaffeThe dataset description is given in Table 1 To guaran-

tee that the testing dataset was comparable to practicalsituations the items in the FOD testing dataset mustmeet the following criteria All samples in the FOD testingdataset should have been collected at the airport or theinstitutional campus The testing samples did not overlapwith those used for training Furthermore samples hadvarious appearances within the same material categoryPlease refer to Fig 3 for the testing samples

Figure 9 shows that for the metal recognition TheIAMF model enhanced the recognition accuracy by 19over AMF and by 4286 over AM for metal recognitionThe results prove the effectiveness of the IAMF modeland the importance of the FOD dataset However we alsonoted that considerable room for improvement existsbecause the best accuracy value was only 67 For plasticand concrete material recognition the improved modelyielded similar performance to that of AMFTable 4 shows the confusion matrix of the IAMF

model It is obvious that metallic FOD was most easilymisclassified as concrete objects We inferred that theconcrete runway and taxiway might act as recognitionnoise because they were the backgrounds in the imagesFor the case of plastic FOD metal and plastic were easilyconfused for each other We inferred that metals andplastics have similar characteristics under the condition ofstrong light illuminationWe further examined the deep features of these three

D-CNN models to understand the reasons for theperformance differences Deep feature the degree of aneuronrsquos excitation is defined as the input value to the

Table 4 Confusion matrix of the IAMF

Label Predicted label

Metal () Plastic () Concrete ()

Metal 6667 1143 2190

Plastic 2213 6766 1021

Concrete 100 000 9900

105 340 440Image ID-10

-5

0

5

10

15

20

Out

put V

alue

AlexNet model trained by MINC (AM)

Neuron 1Neuron 2Neuron 3

105 340 440Image ID-10

-5

0

5

10

15

20

Out

put V

alue

AlexNet model trained by MINC and FOD (AMF)


105 340 440Image ID-10

-5

0

5

10

15

20

Out

put V

alue

Improved AlexNet model trained by MINC and FOD (IAMF)


Fig 10 The neural output value distributions of three convolutional neural networks


last softmax layer Neuronal excitation values were takenfrom layer 8 of the AM layer 8 of the AMF and layer 9of the IAMF over the test images Figure 10 comparesthe deep features of the three D-CNN models over thetest images where the horizontal axis means their imageIDs and the vertical axis denotes the value of deepfeatures From top to bottom three rows of Fig 10demonstrate the results for AM AMF and IAMFrespectively The metal image IDs are from No 1 to No105 No 106 to 340 indicates the plastic image IDs andothers are for the concrete image IDsWe found that the ability to discriminate material was

based on the degree of neuronal excitation For examplea neuron might have been excited for metal but not forplastic To judge the discrimination ability of the D-CNNmodel we observed the distributions of the differentneuronsrsquo excitations The higher the value of a certainneuronrsquos excitation compared to others the stronger theability to discriminate a certain material For exampleaccording to the red circles in Fig 10 the values ofNeuron 3 were better than the values of Neuron 1 andNeuron 2 for concrete images (image ID 341ndash440) Thusthe AFM model had stronger ability to discriminateconcrete According to the green circles in Fig 10compared with the AM model and the AMF modelNeuron 1 of the IAMF model had better excitation valuesthan the other neurons for metal images (image ID 1ndash105)As a result the IAMF model had stronger discriminationability for metal than other models Besides the IAMFmodel had a more concentrated neuron excitation distribu-tion indicating that the IAMF model had a more stablediscrimination ability for FOD material Therefore thediscrimination abilities of the three D-CNN models forFOD gradually increased from the AM model to the IAMFmodel The result also confirmed the effectiveness of theIAMF model

5 ConclusionsFOD material recognition is a challenging and significanttask that must be performed to ensure airport safetyThe general material recognition dataset is not applicableto FOD material recognition Therefore a new FODdataset was constructed in this study The FOD datasetwas different from previous material recognition datasetsin that all training and testing samples were collected inoutdoor environments eg on a runway on a taxiway oron campus We compared the performances of three well-known D-CNN models on the new dataset The resultswere far from acceptable especially for the recogni-tion of metal which accounts for 60 of all FOD Animproved D-CNN model was then introduced andcompared with AlexNet The new model achieved a386 improvement over AlexNet in terms of the rec-ognition of metal FOD

We also inferred that concrete backgrounds can adverselyaffect the FOD material recognition performance leadingto the misclassification of metal or plastic as concreteTherefore our future work will investigate possibleapproaches to introduce image segmentation to distinguishmetal and plastic from concrete Other technologies suchas radar or infrared imaging may be required for betterrecognition results

AcknowledgementsNot applicable

FundingThis work was supported by the National Natural Science Foundation ofChina (No 61170155)This work was also supported by the Science amp Technology Commission ofShanghai Municipality (No 15DZ1100502)

Availability of data and materialsThe FOD material dataset can be downloaded from Google DriveLink httpsdrivegooglecomfiled1UTxZQUipkX6_rC9AoCaweeeeOPrOG1_Bviewusp=sharing

Authorsrsquo contributionsHYX proposed the framework of this work and drafted the manuscript ZQHdesigned the proposed algorithm and performed all the experiments SLFand YCF offered useful suggestions and helped in modifying the manuscriptZH helped in drafting the manuscript All authors read and approved thefinal manuscript

Competing interestsThe authors declare that they have no competing interests

Publisherrsquos NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations

Author details1Shanghai Advanced Research Institute Chinese Academy of SciencesShanghai 201210 China 2University of Chinese Academy of Sciences Beijing100049 China 3School of Computer Engineering and Sciences ShanghaiUniversity Shanghai 200444 China

Received 6 April 2017 Accepted 15 March 2018

References1 M J ODonnell ldquoAirport foreign object debris (FOD) detection equipmentrdquo

FAA AC (1505220) vol 24 (2009)2 WIKI Foreign object damage httpsenwikipediaorgwikiForeign_object_

damage Accessed Nov 20163 CAAC Airport Division et al ldquoFOD Prevention Manualrdquo (2009)4 H Zhang et al The current status and inspiration of FOD industry in

international civil aviation Civ Aviat Manage 295 58ndash61 (2015)5 Z Han et al A novel FOD classification system based on visual features In

International Conference on Image and Graphics LNCS Vol 9217 (2015) pp288ndash296 httpsdoiorg101007978-3-319-21978-3_26

6 Z Han Y Fang H Xu Fusion of low-level feature for FOD classification In 10thInternational Conference on Communications and Networking in China(2015) pp 465ndash469 httpsdoiorg101109CHINACOM20157497985

7 M Jehle C Sommer B Jaumlhne Learning of optimal illumination for materialclassification In 32nd Annual Symposium of the German Association forPattern Recognition LNCS Vol 6376 (2010) pp 563ndash572 httpsdoiorg101007978-3-642-15986-2_57

8 C Liu J Gu Discriminative illumination per-pixel classification of raw materialsbased on optimal projections of spectral BRDF IEEE Trans Pattern Anal MachIntell 36(1) 86ndash98 (2014) httpsdoiorg101109TPAMI2013110


9 C Liu G Yang J Gu Learning discriminative illumination and filters for rawmaterial classification with optimal projections of bidirectional texturefunctions In Computer Vision and Pattern Recognition (2013) pp 1430ndash1437 httpsdoiorg101109CVPR2013188

10 H Zhang K Dana K Nishino Reflectance hashing for material recognition InComputer Vision and Pattern Recognition (2015) pp 3071ndash3080 httpsdoiorg101109CVPR20157298926

11 J Filip P Somol Materials classification using sparse gray-scale bidirectionalreflectance measurements International Conference on Computer Analysis ofImages and Patterns LNCS Vol 9257 (2015) pp 289ndash299 httpsdoiorg101007978-3-319-23117-4

12 E Hayman B Caputo M Fritz JO Eklundh On trhe significance of real-worldconditions for material classification In European Conference on ComputerVision LNCS Vol 3024 (2004) pp 253ndash266 httpsdoiorg101007978-3-540-24673-2_21

13 B Caputo E Hayman M Fritz JO Eklundh Classifying materials in the realworld Image Vis Comput 28(1) 150ndash163 (2010) httpsdoiorg101016jimavis200905005

14 B Caputo E Hayman P Mallikarjuna Class-specific material categorization In10th IEEE International Conference on Computer Vision Proc IEEE Int ConfComput Vision II (2005) pp 1597ndash1604 httpsdoiorg101109ICCV200554

15 M Varma A Zisserman Classifying images of materials achievingviewpoint and illumination independence In European Conference onComputer Vision LNCS Vol 2352 (2002) pp 255ndash271 httpsdoiorg1010073-540-47977-5_17

16 M Varma A Zisserman A statistical approach to material classification usingimage patch exemplars IEEE Trans Pattern Anal Mach Intell 31(11) 2032ndash2047 (2009) httpsdoiorg101109TPAMI2008182

17 M Varma A Zisserman A statistical approach to texture classification fromsingle images Int J Comput Vis 62(1-2) 61ndash81 (2005) httpsdoiorg101023BVISI000004658939864ee

18 L Liu PW Fieguth D Hu Y Wei G Kuang Fusing sorted random projectionsfor robust texture and material classification IEEE Trans Circuits Syst VidTechnol 25(3) 482ndash496 (2015) httpsdoiorg101109TCSVT20142359098

19 D Hu L Bo Toward robust material recognition for everyday objects In BritishMachine Vision Conference Proc BMVC (2011) pp 1ndash11 httpsdoiorg105244C2548

20 C Liu L Sharan EH Adelson R Rosenholtz Exploring features in a Bayesianframework for material recognition In 2010 IEEE Conference on ComputerVision and Pattern Recognition Proc CVPR (2010) pp 239ndash246 httpsdoiorg101109CVPR20105540207

21 L Sharan C Liu R Rosenholtz EH Adelson Recognizing materials usingperceptually inspired features Int J Comput Vis 103(3) 348ndash371 (2013)httpsdoiorg101007s11263-013-0609-0

22 L Sharan R Rosenholtz EH Adelson Accuracy and speed of materialcategorization in real-world images J Vis 14(9) 1ndash24 (2014) httpsdoiorg10116714912

23 M Cimpoi et al Describing textures in the wild In 2014 IEEE Conference onComputer Vision and Pattern Recognition Proc CVPR (2014) pp 3606ndash3613httpsdoiorg101109CVPR2014461

24 M Cimpoi S Maji A Vedaldi Deep filter banks for texture recognition andsegmentation In 2015 IEEE Conference on Computer Vision and PatternRecognition Proc CVPR (2015) pp 3828ndash3836 httpsdoiorg101109CVPR20157299007

25 S Bell P Upchurch N Snavely K Bala Material recognition in the wild withthe materials in context database In 2015 IEEE Conference on ComputerVision and Pattern Recognition Proc CVPR (2015) pp 3479ndash3487 httpsdoiorg101109CVPR20157298970

26 Y Zhang et al ldquoIntegrating deep features for material recognitionrdquo (2015)[arXiv151106522]

27 H Son C Kim N Hwang C Kim Y Kang Classification of major constructionmaterials in construction environments using ensemble classifiers Adv EngInform 28(1) 1ndash10 (2014) httpsdoiorg101016jaei201310001

28 A Dimitrov M Golparvar-Fard Vision-based material recognition forautomated monitoring of construction progress and generating buildinginformation modeling from unordered site image collections Adv EngInform 28(1) 37ndash49 (2014) httpsdoiorg101016jaei201311002

29 JJ Koenderink et al Bidirectional reflection distribution function ofthoroughly pitted surfaces Int J Comput Vis 31(2-3) 129ndash144 (1999)httpsdoiorg101023A1008061730969

30 M Oren Generalization of the Lambertian model and implications formachine vision Int J Comput Vis 14(3) 227ndash251 (1995) httpsdoiorg101007BF01679684

31 GE Hinton et al A fast learning algorithm for deep belief nets Neural Comput18(7) 1527ndash1554 (2006) httpsdoiorg101162neco20061871527

32 K He et al ldquoDeep residual learning for image recognitionrdquo (2015) [arXiv151203385]

33 O Russakovsky et al ImageNet large scale visual recognition challenge Int JComput Vis 115(3) 211ndash252 (2015) httpsdoiorg101007s11263-015-0816-y

34 J Deng et al ImageNet a large-scale hierarchical image database In 2009IEEE Conference on Computer Vision and Pattern Recognition Proc CVPR(2009) pp 248ndash255 httpsdoiorg101109CVPR20095206848]

35 A Krizhevsky I Sutskever GE Hinton ImageNet classification with deepconvolutional neural networks In 26th Annual Conference on NeuralInformation Processing Systems Adv Neural Inf Proces Syst (2012) pp1097ndash1105

36 C Szegedy et al Going deeper with convolutions In 2015 IEEE Conference onComputer Vision and Pattern Recognition Proc CVPR (2015) pp 1ndash9httpsdoiorg101109CVPR20157298594]

37 C Yan et al Effective Uyghur language text detection in complex backgroundimages for traffic prompt identification IEEE Trans Intell Transport Syst 19(1)220ndash229 (2018) httpsdoiorg101109TITS20172749977

38 Labeled Faces in the Wild httpvis-wwwcsumassedulfwresultshtmlglasssixAccessed Sept 2017

39 Large Scale Visual Recognition Challenge 2017 (ILSVRC2017) Task 1a Objectdetection with provided training data httpimage-netorgchallengesLSVRC2017results Accessed Sept 2017

40 Y Chen J Li H Xiao et al ldquoDual path networksrdquo (2017) [arXiv170701629]41 C Yan et al Supervised hash coding with deep neural network for

environment perception of intelligent vehicles IEEE Transactions on IntelligentTransportation Systems (2018) httpsdoiorg101109TITS20172749965

42 K Simonyan and A Zisserman ldquoVery deep convolutional networks for large-scale image recognitionrdquo (2014) [arXiv14091556]

43 L Shao F Zhu X Li Transfer learning for visual categorization a survey IEEETrans Neural Netw Learn Syst 26(5) 1019ndash1034 (2015)

44 F Zhuang et al Survey on transfer learning research J Software 26(1) 26ndash39 (2015) httpsdoiorg1013328jcnkijos004631

45 Y Sun X Wang X Tang Deeply learned face representations are sparseselective and robust Proceedings of the IEEE Conference on ComputerVision and Pattern Recognition Proc CVPR (2015) pp 2892ndash2900

46 A K Reyes et al ldquoFine-tuning deep convolutional networks for plantrecognitionrdquo CLEF (Working Notes) 2015

47 P Sermanet et al ldquoOverfeat Integrated recognition localization anddetection using convolutional networksrdquo [arXiv13126229] (2013)

48 V Mnih et al Human-level control through deep reinforcement learningNature 518(7540) 529ndash533 (2015)

49 Y Jia et al Caffe convolutional architecture for fast feature embedding In2014 ACM Conference on Multimedia Proc ACM Conf Multimedia (2014)pp 675ndash678 httpsdoiorg10114526478682654889

50 C Yan Y Zhang J Xu et al A highly parallel framework for HEVC codingunit partitioning tree decision on many-core processors IEEE SignalProcess Letters 21(5) 573ndash576 (2014)

51 C Yan et al Efficient parallel framework for HEVC motion estimation onmany-core processors IEEE Trans Circuits Syst Vid Technol 24(12) 2077ndash2089 (2014)

52 C Yan et al Parallel deblocking filter for HEVC on many-core processorElectron Lett 50(5) 367ndash368 (2014)

53 C Yan et al Efficient parallel HEVC intra-prediction on many-core processorElectron Lett 50(11) 805ndash806 (2014)

Xu et al EURASIP Journal on Image and Video Processing (2018) 201821 0 of 10

Abstract

Introduction

Related work

Method

Dataset construction

Choice of the D-CNN model

Transfer learning

Improved D-CNN based on AlexNet

Experimental results and discussion


Results of the improved model

Conclusions

Funding

Availability of data and materials

Authorsrsquo contributions

Competing interests

Publisherrsquos Note

Author details

References

material recognition can be broadly categorized as hand-crafted or automatic feature extraction Hand-craftedapproaches can be further divided into surface reflectance[7ndash11] 3D texture [12ndash19] and feature fusion [20ndash22]approaches Automatic feature extraction approaches referto those that involve acquiring image features using a deepconvolutional neural network (D-CNN) [23ndash26]There are some correlations between the surface

reflectance properties and the categories of materialsFor example wooden surfaces tend to be brownwhereas metallic ones tend to be shiny However differentclasses of materials may exhibit similar reflectance proper-ties such as the translucence of plastic wax and glassConsequently understanding an objectrsquos reflectanceproperties is insufficient to determine its material consti-tution Similarly different materials may have the same3D texture pattern as shown in Fig 2 To overcome thesechallenges researchers have attempted to combinedifferent features or have attempted automatic featureextraction to perform material recognition tasks Someremarkable results were obtained on some specific datasetsin certain studiesHowever past research results remain inadequate to

meet the demands of FOD material recognition Firstthere is no specific FOD dataset for the task because ofthe unique airport environment Although Bell et al [25]used more than 300 million image patches for trainingthe images were acquired mainly in indoor environmentswhere light conditions are quite different from the FODemergence locations The results were hence quite poor

when these 300 million image patches were used fortraining while FOD images were used for testing (pleaserefer to the ldquoSection 4rdquo for details) Second a high-recognition ratio is necessary for metal recognitionMetallic objects are far more harmful than othermaterials Meanwhile 60 of FOD is constituted by metal[1] However according to prior results [19 21] therecognition rate was quite low for metallic objectsThis paper proposes a novel FOD material recognition

approach based on transfer learning and a mainstreamdeep convolutional neural network (D-CNN) modelThis paper describes an FOD image dataset consisting ofimages taken on the runways of Shanghai HongqiaoInternational Airport and the campus of our researchinstitute The dataset consisted of 3470 images dividedinto three categories by material metal concrete andplastic The proposed approach is optimized torecognize metal because of the high risk that is due toits high-damage level to aircrafts and its high occurrencefrequency in airportsThis research will help improve the intelligence

capability the ease of using and the user experience ofFOD detection systems It will also encourage moreapplications of material recognition systems especiallyin security and manufacturing such as construction sitemanagement [27 28]The rest of this paper is organized as follows Section

2 introduces related work and our approach is describedin Section 3 Section 4 presents a discussion of theexperiment results and Section 5 summarizes ourconclusion and plan for future work

2 Related workMaterial recognition a fundamental problem in computervision has a wide range of applications For example anautonomous vehicle or a mobile robot can make decisionson whether a forthcoming terrain is asphalt gravel ice orgrass A cleaning robot can distinguish among wood tileor carpet The approaches to material recognition arebroadly divided into two categories according to featureextraction methods hand-crafted features and automaticfeatures Hand-crafted approaches can be further dividedinto surface reflectance-based 3D texture-based andfeature fusion-based approaches Automatic featureextraction approaches refer to those acquiring imagefeatures through a D-CNNThe most popular formalization for model surface

reflectance is the bidirectional reflectance distributionfunction (BRDF) This function defines the amount oflight reflected at a given point on a surface for anycombination of incidence and reflection angles [21] TheBRDF has a parametric type [29 30] and an empiricaltype [7ndash9 11] Parametric BRDF models cannot acquirea broad set of real-world reflectance properties In

Fig 1 The twisted metal strip that caused Air France Flight 4590 to crash

Fig 2 Surfaces with similar textures may be composed of differentmaterials These objects are made of fabric plastic and paper fromleft to right [21]

Xu et al EURASIP Journal on Image and Video Processing (2018) 201821 of 10


















Metal 30 1000 105

Plastic 0 1000 235

Concrete 0 1000 100











Input (RGB image) 227227 224224 224224 227227


11114 331 772 11114

331

Max pooling 332 222 332 332


551 331 331 551

331

Max pooling 332 222 332 332




331 111 331

Max pooling 332 222 332 332


331 Inception(4a)

331 Inception(4b)

111 Inception(4c)

Inception(4d)

Inception(4e)

Max pooling 222 332


331 Inception(5a)

331 Inception(5b)

111

Pooling Max pool222

Average pool771

Linear FC-4096 FC-4096 FC-1000 FC-4096

FC-4096 FC-4096 FC-4096

FC-1000 FC-1000 FC-1000

FC-3






eth1THORN





Fig 5 RELU function

Fig 6 Pooling





y frac14 arg maxi

eziPNjfrac141e

z j

eth2THORN









AlexNet 0053 0013 0 0026 0132 0132 0092 0289 0263

VGG-16 0 0 0 0 0053 0053 0447 0355 0092

GoogLeNet 0 0 0013 0 0 0066 0605 0276 0039











Metal 6667 1143 2190

Plastic 2213 6766 1021

Concrete 100 000 9900

105 340 440Image ID-10

-5

0

5

10

15

20

Out

put V

alue



105 340 440Image ID-10

-5

0

5

10

15

20

Out

put V

alue



105 340 440Image ID-10

-5

0

5

10

15

20

Out

put V

alue








































































Abstract

Introduction

Related work

Method



Transfer learning





Conclusions

Funding



Competing interests


Author details

References


















Metal 30 1000 105

Plastic 0 1000 235

Concrete 0 1000 100











Input (RGB image) 227227 224224 224224 227227


11114 331 772 11114

331

Max pooling 332 222 332 332


551 331 331 551

331

Max pooling 332 222 332 332




331 111 331

Max pooling 332 222 332 332


331 Inception(4a)

331 Inception(4b)

111 Inception(4c)

Inception(4d)

Inception(4e)

Max pooling 222 332


331 Inception(5a)

331 Inception(5b)

111

Pooling Max pool222

Average pool771

Linear FC-4096 FC-4096 FC-1000 FC-4096

FC-4096 FC-4096 FC-4096

FC-1000 FC-1000 FC-1000

FC-3






eth1THORN





Fig 5 RELU function

Fig 6 Pooling





y frac14 arg maxi

eziPNjfrac141e

z j

eth2THORN









AlexNet 0053 0013 0 0026 0132 0132 0092 0289 0263

VGG-16 0 0 0 0 0053 0053 0447 0355 0092

GoogLeNet 0 0 0013 0 0 0066 0605 0276 0039











Metal 6667 1143 2190

Plastic 2213 6766 1021

Concrete 100 000 9900

105 340 440Image ID-10

-5

0

5

10

15

20

Out

put V

alue



105 340 440Image ID-10

-5

0

5

10

15

20

Out

put V

alue



105 340 440Image ID-10

-5

0

5

10

15

20

Out

put V

alue








































































Abstract

Introduction

Related work

Method



Transfer learning





Conclusions

Funding



Competing interests


Author details

References










Input (RGB image) 227227 224224 224224 227227


11114 331 772 11114

331

Max pooling 332 222 332 332


551 331 331 551

331

Max pooling 332 222 332 332




331 111 331

Max pooling 332 222 332 332


331 Inception(4a)

331 Inception(4b)

111 Inception(4c)

Inception(4d)

Inception(4e)

Max pooling 222 332


331 Inception(5a)

331 Inception(5b)

111

Pooling Max pool222

Average pool771

Linear FC-4096 FC-4096 FC-1000 FC-4096

FC-4096 FC-4096 FC-4096

FC-1000 FC-1000 FC-1000

FC-3






eth1THORN





Fig 5 RELU function

Fig 6 Pooling





y frac14 arg maxi

eziPNjfrac141e

z j

eth2THORN









AlexNet 0053 0013 0 0026 0132 0132 0092 0289 0263

VGG-16 0 0 0 0 0053 0053 0447 0355 0092

GoogLeNet 0 0 0013 0 0 0066 0605 0276 0039











Metal 6667 1143 2190

Plastic 2213 6766 1021

Concrete 100 000 9900

105 340 440Image ID-10

-5

0

5

10

15

20

Out

put V

alue



105 340 440Image ID-10

-5

0

5

10

15

20

Out

put V

alue



105 340 440Image ID-10

-5

0

5

10

15

20

Out

put V

alue








































































Abstract

Introduction

Related work

Method



Transfer learning





Conclusions

Funding



Competing interests


Author details

References




eth1THORN





Fig 5 RELU function

Fig 6 Pooling





y frac14 arg maxi

eziPNjfrac141e

z j

eth2THORN









AlexNet 0053 0013 0 0026 0132 0132 0092 0289 0263

VGG-16 0 0 0 0 0053 0053 0447 0355 0092

GoogLeNet 0 0 0013 0 0 0066 0605 0276 0039











Metal 6667 1143 2190

Plastic 2213 6766 1021

Concrete 100 000 9900

105 340 440Image ID-10

-5

0

5

10

15

20

Out

put V

alue



105 340 440Image ID-10

-5

0

5

10

15

20

Out

put V

alue



105 340 440Image ID-10

-5

0

5

10

15

20

Out

put V

alue








































































Abstract

Introduction

Related work

Method



Transfer learning





Conclusions

Funding



Competing interests


Author details

References


y frac14 arg maxi

eziPNjfrac141e

z j

eth2THORN









AlexNet 0053 0013 0 0026 0132 0132 0092 0289 0263

VGG-16 0 0 0 0 0053 0053 0447 0355 0092

GoogLeNet 0 0 0013 0 0 0066 0605 0276 0039











Metal 6667 1143 2190

Plastic 2213 6766 1021

Concrete 100 000 9900

105 340 440Image ID-10

-5

0

5

10

15

20

Out

put V

alue



105 340 440Image ID-10

-5

0

5

10

15

20

Out

put V

alue



105 340 440Image ID-10

-5

0

5

10

15

20

Out

put V

alue








































































Abstract

Introduction

Related work

Method



Transfer learning





Conclusions

Funding



Competing interests


Author details

References









Metal 6667 1143 2190

Plastic 2213 6766 1021

Concrete 100 000 9900

105 340 440Image ID-10

-5

0

5

10

15

20

Out

put V

alue



105 340 440Image ID-10

-5

0

5

10

15

20

Out

put V

alue



105 340 440Image ID-10

-5

0

5

10

15

20

Out

put V

alue








































































Abstract

Introduction

Related work

Method



Transfer learning





Conclusions

Funding



Competing interests


Author details

References




































































Abstract

Introduction

Related work

Method



Transfer learning





Conclusions

Funding



Competing interests


Author details

References

foreign object debris material recognition based on

Documents