pembuatan aplikasi pengenalan plat nomor kendaraan dengan metode jaringan syaraf tiruan

PEMBUATAN APLIKASI PENGENALAN PLAT NOMOR KENDARAAN DENGAN METODE JARINGAN SYARAF TIRUAN

Hendra (26406129)

Background

• In the present day license plate has an important role in a parking security systems.

• The use of computers and cameras as a security system is increasing.

• Application that can automatically recognize the license plates of vehicles through the images captured by the camera.

Objective

• Create an application that facilitates the control of the parking system.

• This application will be able to recognize license plates from a camera image, using image processing and artificial neural networks.

Application Process

Image Processing

• Image processing is a method to managing or manipulating images in the form of two-dimensional (digital image).

• Image processing used in this application :– Grayscaling– Thresholding– Plate Finder Algorithm (Smearing)– Thinning

Grayscaling (1)

• Grayscaling used to convert color images into grayscale images by calculating the average color elements of Red, Green, and Blue.

• Function :

Grayscale = ((WR*R)+( WG*G)+( WB*B)) / 256

where: WR = Weight for red,

WG = Weight for green,

WB = Weight for Blue,

R = Red value,

G = Green Value,

B = Blue.

Grayscaling (2)

• Result :

Color Image Grayscale image

Thresholding (1)

• Thresholding used to convert grayscale image into black and white image.

• In thresholding there are threshold value, pixel that have values below the threshold value will be changed to 0 (black), and pixels that have a value above the threshold was changed to 255 (white).

Thresholding (2)

• Result :

Grayscale Image Threshold Image

Plate Finder Algorithm (Smearing)

• Smearing is a method for extracting the location of text in an image.

• Smearing will process the image vertically and horizontally (scan-lines). If the number of consecutive black pixels is less than the specified threshold then the image will be changed to white.

Plate Finder Algorithm (Smearing)

• For Example :

If the limit value has been determined 10 and 100 for horizontal and vertical smearing.

If number of ‘black’ pixels < 10 ; pixels become ‘white’

Else ; no change

If number of ‘black’ pixels > 100 ; pixels become ‘white’

Else ; no change

Plate Finder Algorithm (Smearing)

• In this application to find the locatian of license plate in an image, smearing is running three times :– Smearing to find black pixel between character.– Smearing to find white pixel is detected as character.– Smearing to find black pixel is detected as license

plate .

Plate Finder Algorithm (Smearing)

Smearing I Smearing II

Smearing III Merge Smearing I & II

Character Segmentation

• Character segmentation used to divide each character in plate image.

• In this process each line of pixels scan vertically. • If the line pixel is detected as the beginning of of

characters the value is 1.• If the line pixel is detected as the end of

character the value is 2.• If the line pixel is detected as the middle of

character the value is 3.

Character Segmentation

Plate Image

Segmentation Result

Encode Weight

• Encode weight used to get the weight to be processed in neural network.

• By using the center of mass for each region will be divided on 30 degrees.

• For each region, white pixel will be calculated and its percentage is calculated.

Encode Weight

Self Organizing Maps

• Self Organizing Maps (SOM) is a type of artificial neural network to cluster the data that has similarities with using an environment map.

• From the results of clustering data we can get any information about the output of each cluster, in this application the output information is character recognize.

2 Dimension Map SOM

Component:– Input– Weight Matrix– Array– Distance formula– Weight evaluation

Process :– Training– Testing

Training

1. Initialization of input neurons :

x1, x2, x3, ..., xi

2. Initialize output neurons (2 dimensional) :

y11, y12, y13, ..., yjl.

3. Initialize weight matrix between input neuron and output neuron (ijl), with vrandom value between 0 and 1.

Training

4. Repeat step 5 to 8 until no change in the weight map or iteration has reached the maximum iteration.5. Choose an input from the existing input vectors.6. Calculating the distance between the input vector of weight (djl) with

each neuron output with Euclidean distance formula:

7. From the calculating distance choose the smallest value. The smallest value is winning neuron (most similar map).

8. For each connection weights updated using this formula:

9. Save convergen weight.

Learning Rate ((t))

• Shows how the adaptation of learning to the data. Adaptation scalar valued function 0 ≤ (t) ≤ 1.

(t) will approach to 0, weight changes will be smaller and the input vectors can be mapped properly.

• Function:

Where: t number of current iteration; T max iteration; learning rate decrease value

)1()1()( Tt

t

Neighborhood Function

• Neighborhood function have value between 0 and 1.• Gaussian function is used to find neighoorhood value:

• |rij – rbc| is the distance between the neurons that will be changed to the winning neuron.

(t) is the kernel width (the width dimensions of the output neurons) that are influenced by (multiplied by) learning rate (t).

• This change is to get the neurons to a stable position / convergence.

)(2exp

2

2

)( t

rrh

bcij

tijbc

Testing (Clustering)

• Testing Algorithm is same like training algorithm without update weight matrix.

• Testing algorithm:– Initialize weight matrix ijl with convergent weight (training result)

– Clustering data vectors (use before) like training process, but without updating the weights.

– Define the output specifications of each cluster.– Enter the new data vector in the test and do clustering for all the

new data vector.– Update output specifications of each new data with the output

specifications of each cluster, where he is mapped.

Database Store

• Data storage in this application using txt files.

Database Name Information

DataSample.txt Save the weight of each region of added data sample.

SampleCount.txt Save count of data that existed at DataSample.txt.

NeuronW.txt Save the convergent weights between input neurons and output the result of training.

DataSampleTraining.txt Storing the data sample used of training.

DataPosition.txt Saving location of data on the SOM map.

OutputMap.txt Save the output specification from each map of the training results.

TrainOption.txt Save training setting.

Voting.txt Storing voting data calculation to get the specification output of each map.

Application Menu

Testing for Contrast Images

• In this application need a picture with the high contrast between plate character and background plate (bright characters and a dark background) and between the car color and license plate color.

Car Color and License Plate with low contrast

Reason :Smearing process based on the calculationof plate area. If a dark color car then smearing process is difficult to separate between plate area and car body.

Testing for Contrast Images

• Lighting must be balanced in plate area.

Low Lighting High Lighting

Testing for Contrast Images

Balance Lighting

Testing for Distance Taking Pictures

• This application requires images taken from a distance of ±2 meters.

• Reason :Smearing method is a method of detecting a character in specific range. In this application plate that can be detected with size between 90-190 pixels for width, and 15-55 pixels for height.

Testing for Distance Taking Pictures

Testing for Skew Angle

• This application can only detect the plate with 0 degree skew.

• Reason :Smearing is a scan-line method, when the plate is not in 0 degree, there will be intersection in plate location.

Testing for Skew Angle

5 Degree Skew Angle

Testing for Process Training SOM

• Larger width of the SOM map and larger amount of data need more iteration and time for the training process.

• Training process time and result is influenced by several factors:– the initial random weight– width of the SOM map– the number of data sample

• Between the map width and amount of data sample has a close relationship.

Testing for Recognize

• By using the results of voting winner this application can recognize up to 78% for the overall character recognition with data sample has been trained before.

• Test results based on voting winner this application can recognize characters 62% with new data.

• From the test result the accuracy of the SOM clustering algorithm in this application is 70,042% by looking at their environment map.

Testing for Recognize

• Causes of failure recognition:– Some letters that have similarity form.– Different forms of writing license plates.– Font size difference.– Difference thickness of the letters– different forms of font.

Conclusion

• Time required for image processing of about 7-10 sec.

• Training required minimum map size 7x7 (49 boxes).

• Smearing algorithm can only detect the field with a box-shaped with a certain range

• Tthe best threshold value is 100 for the best thresholding result for this application.

Suggestion

• Before searching the license plate location in the picture should be a process for detecting the skew angle of the license plate.

• With the successful equal to 70%, the SOM algorithm is not suitable for the recognition process.

• The input image is not too big because it will take a long processing time, is also not too small because it will be difficult to recognize.

• Regioning process not only done by one method.