classiﬁcation at 10hz: protecting high-power lasers with deep … · 2020. 5. 7. ·...

Classification at 10Hz: Protecting High-PowerLasers with Deep Learning

Jeffrey Kelling,René Gebhardt, Uwe Helbig, Stefan Bock,

Ulrich Schramm, Guido Juckeland

10th October 2017

Jeffrey Kelling, René Gebhardt, Uwe Helbig, Stefan Bock, Ulrich Schramm, Guido Juckeland | FWCC | http//www.hzdr.deMember of the Helmholtz Association

Where am I from?

outside of Dresden, Germany

Jürgen-M. Schulter http://dresden-luftfoto.de

major facilities @ HZDR:radiation source ELBEDresden high magnetic fieldlaboratorycenter for positron emissiontomographyion beam centerhigh-power laser-particleacceleration

about me:member of computationalscience groupbackground in statistical andtheoretical solid state physics

Jeffrey Kelling, René Gebhardt, Uwe Helbig, Stefan Bock, Ulrich Schramm, Guido Juckeland | FWCC | http//www.hzdr.deMember of the Helmholtz AssociationPage 1/34

1 Motivation: Perils that high-power lasers face

2 Introduction: Our deep-learning problem

3 A feature-detection–based approach to detect critical states

4 Results: Generalization ability of trained GoogLeNet

5 Outlook: Future approaches

6 Summary and conclusions


Motivation: Perils that high-power lasers face








High-Power Lasers @ HZDR

lasers DRACO and PENELOPE (under construction)up to 150TW and 1 PW beam power, respectivelyfemto-second pulses at 10Hz

applications:investigation of matter in high electric fieldsparticle acceleration...


Aging of Components and Damage Spreading

optical components age due to intense lightdamaged mirrors or lenses diffract light differently

added focal spots may hit other componentsdeposition of too much energy leads to accelerated aging

mirrors and lenses are expensive,non-linear crystals cost even more and are hard to replace


Cross Sections of Unfocussed Beams

the beam can be monitored by cameras behind partiallytransparent mirrorsafter failing of a mirror inhomogeneities in the form of distortedconcentric rings appeardiffraction maxima of these can damage other components


Cross Sections of Unfocussed Beams

the signaling patterns can be identified by humansrecognition and reaction must happen between consecutive pulsesat 10Hz, under 0.1 s

proof of concept for detecting visible signs of broken mirror


Introduction: Our deep-learning problem








Deep Learning

https://xkcd.com/1838/

key points to settle:what to classify / predict?data

quantitymust be representative ofthe problem

networksize / architechturetrainingability to generalize

( this is a cyle


The Data

problems:1 limited variety of training images—effectively one with features2 similar features may also appear during normal operation) whole-image classification not an option) unsupervised learning for anomaly-detection tricky because even

good images can contain “anomalies”


Training Image Classification with,like, Two Images

... for classification at 10Hz

Jeffrey Kelling,René Gebhardt, Uwe Helbig, Stefan Bock,

Ulrich Schramm, Guido Juckeland

10th October 2017

Jeffrey Kelling, René Gebhardt, Uwe Helbig, Stefan Bock, Ulrich Schramm, Guido Juckeland | FWCC | http//www.hzdr.deMember of the Helmholtz Association

The Network

input

Conv7x7+2(S)

MaxPool3x3+2(S)

LocalRespNorm

Conv1x1+1(V)

Conv3x3+1(S)

LocalRespNorm

MaxPool3x3+2(S)

Conv1x1+1(S)

Conv1x1+1(S)

Conv1x1+1(S)

MaxPool3x3+1(S)

DepthConcat

Conv3x3+1(S)

Conv5x5+1(S)

Conv1x1+1(S)

Conv1x1+1(S)

Conv1x1+1(S)

Conv1x1+1(S)

MaxPool3x3+1(S)

DepthConcat

Conv3x3+1(S)

Conv5x5+1(S)

Conv1x1+1(S)

MaxPool3x3+2(S)

Conv1x1+1(S)

Conv1x1+1(S)

Conv1x1+1(S)

MaxPool3x3+1(S)

DepthConcat

Conv3x3+1(S)

Conv5x5+1(S)

Conv1x1+1(S)

Conv1x1+1(S)

Conv1x1+1(S)

Conv1x1+1(S)

MaxPool3x3+1(S)

AveragePool5x5+3(V)

DepthConcat

Conv3x3+1(S)

Conv5x5+1(S)

Conv1x1+1(S)

Conv1x1+1(S)

Conv1x1+1(S)

Conv1x1+1(S)

MaxPool3x3+1(S)

DepthConcat

Conv3x3+1(S)

Conv5x5+1(S)

Conv1x1+1(S)

Conv1x1+1(S)

Conv1x1+1(S)

Conv1x1+1(S)

MaxPool3x3+1(S)

DepthConcat

Conv3x3+1(S)

Conv5x5+1(S)

Conv1x1+1(S)

Conv1x1+1(S)

Conv1x1+1(S)

Conv1x1+1(S)

MaxPool3x3+1(S)

AveragePool5x5+3(V)

DepthConcat

Conv3x3+1(S)

Conv5x5+1(S)

Conv1x1+1(S)

MaxPool3x3+2(S)

Conv1x1+1(S)

Conv1x1+1(S)

Conv1x1+1(S)

MaxPool3x3+1(S)

DepthConcat

Conv3x3+1(S)

Conv5x5+1(S)

Conv1x1+1(S)

Conv1x1+1(S)

Conv1x1+1(S)

Conv1x1+1(S)

MaxPool3x3+1(S)

DepthConcat

Conv3x3+1(S)

Conv5x5+1(S)

Conv1x1+1(S)

AveragePool7x7+1(V)

FC

Conv1x1+1(S)

FC

FC

SoftmaxActivation

softmax0

Conv1x1+1(S)

FC

FC

SoftmaxActivation

softmax1

SoftmaxActivation

softmax2

decided on using GoogLeNet, because:no experience with network design at the timeGoogLeNet1 was proven to be able tolearn features in its lower layers and generalizedesign employs dropout layers to suppress over fitting

counter indications:only about 3 classespatterns not very complex

) smaller network may also do

1C. Szegedy, et al. In Computer Vision and Pattern Recognition (CVPR) (2015).


A feature-detection–based approach to detectcritical states








Segmentation and Object Localization

insufficient training data forimage segmentation

object localization using slidingwidow computationallyexpensivenot enough data to trainnetwork that proposescandidate regions


Region Proposal by Feature Detection

apply band pass filter to detectlarge fluctuationspropose regions around peaks

0 100 200 300 400 500 600-20

-10

0

10

20

y-position [pixels]

inte

nsity

5σ - threshold


Candidate Regions for Classification

candidate regions:at least two separatepatches above 5� orbelow �5� in proximity

square regions (64⇥ 64px)centered around candidatescale down if candidate regionis largerkeep aspect ratio


Heuristics for Feature Classification

classes:true positive, false positive, false positive (edge)heuristics required to classify featureswe have an intuitive understanding of what eachclass looks likedeep-learning was developed for data-driven...

crafting of classifiers anddevelopment of heuristics

) the purpose is to register known pattern andtrigger safety states

human operator makes final decision aboutaction to takeacceptable features can be marked forignoring/monitoring


Interlude

Questions?








Implementation

for production we use C++... python is good for prototyping and data analysis ...

need reaction time below 100ms, high throughput not sufficient

Caffe2 comes with documented C++ APIprovides program for (multi-)GPU accelerated training,no custom code required for training

OpenCV3 for matrix operations, FFTs, image handlinginteroperable with Caffe

2Jia, Y. et al. arXiv preprint arXiv:1408.5093 (2014) / http://caffe.berkeleyvision.org/

3Bradski, G. Dr. Dobb’s Journal of Software Tools (2000) / http://opencv.org


Training: Data

⇠ 389 regions for training, ⇠ 89 for validationdrawn from all images

equivalent to simple augmentation, at best

no way to get actually independent test datafrom this

using a lower quality dataset for testing


Training: Protocol

K20 GPUs used for trainingstochastic gradient descend parameters:batch size 8initial learning rate 0.01learning rate annealing (�) 0.8

training samples randomly mirroredabout 2000 iterations (batches) to reach steady state(no further reduction in training loss)


Classification Performancethe code is a proof of concept and not optimized,so we do not keep below the 100ms mark, yet

filtering, feature-detection, preprocessing is on CPU, single-threadedinference can be offloaded to GPU by Caffe, using cuDNN

performance on i7-4930K @3.4GHz and GTX Titan Black(Kepler)

filtering (CPU) ⇠ 100msfeature detection (CPU) ⇠ 6msclassification per candidate (CPU) ⇠ 50msclassification per candidate (GPU) ⇠ 12ms

total, using GPU classification . 200ms

filtering and feature-detection can be parallelizedclassification can be batched

speeding up the filtering (FFTs) by ⇠ 3⇥ should suffice


Results: Generalization ability of trainedGoogLeNet








“Test Data”

data not seen during training nor validation


“Test Data”: Classification

true positive false positive false positive.edge

network trained to recognize oddly-shaped rings, as intended

no statistics given, due to the small amount of available data


Where it fails...

reserving region around one of three features for validation


Where it fails: Priorities

true positive false positive false positive.edge

network fails to choose classification “true positive” over “edge”or fails to recognize feature near edge

) reserving parts of small data sets for verification/testingeasily increases bias of the training set


Problem with the Training Set

0 1 000 2 000 3 000 4 000 5 000

10

�6

10

�5

10

�4

10

�3

10

�2

10

�1

10

0

iterations (batches)

loss

training lossvalidation loss

we know from the test set, there is no over fitting problem) validation set contains feature not represented in the training set


Outlook: Future approaches








Dust

A tailored solution does not apply to all cases ...

,... detected features barely coincide with dust speckles.Having been trained for broken mirrors before, the network only finds

the classes “false positive” or “edge” here.


Hierarchical Object Localization

object localization using hierarchicalsliding-window approach

classes:one feature, multiple features, no featurestart with large window,split if in class “multiple features”

label images with bounding boxes;label windows automaticallyget more data

different particle arrangementslower particle density, to be closer toreal-live events

smaller network for throughput, if possible) make prior feature-detection obsolete


Anomaly Detection

train (convolutional) autoencoder withminimal latent state

Julien Despois @ medium.com

difference between original and decodedimage could reveal features

training images must not contain anypossibly bad features

) challenging data-acquisition

may still use classifier as second stage) use learned anomaly detection in place of

feature-detection


Summary and conclusions








Summary and Conclusions

development of safety feature for pulsed high-power lasersdetection of signs of damaged mirrors to avoid damage spreading

taking advantage of smoothness of beam under normal conditionsto quickly identify regions of interestdeep CNN GoogLeNet delivers reasonable classificationperformance even when trained with little data

still, more data required to reduce bias in datasetsto obtain more reliable and general classifier

problem looks like a candidate for anomaly detection(auto encoders, GAN, ...)

but, “good” data contains anomalies considered bad in other caseshowever, auto encoders may work for region proposal


End

Thank You.


classiﬁcation at 10hz: protecting high-power lasers with deep … · 2020. 5. 7. ·...

Documents