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Convolutional neural networks are the most widely used deep learningalgorithms for traffic signal classification till date but they fail to capturepose, view, orientation of the images because of the intrinsic inability of maxpooling layer.This paper proposes a novel method for Traffic sign detectionusing deep learning architecture called capsule networks that achievesoutstanding performance on the German traffic sign dataset.Capsule networkconsists of capsules which are a group of neurons representing theinstantiating parameters of an object like the pose and orientation by usingthe dynamic routing and route by agreement algorithms.unlike the previousapproaches of manual feature extraction,multiple deep neural networks with manyparameters,our method eliminates the manual effort and provides resistance tothe spatial variances.CNNs can be fooled easily using various adversary attacksand capsule networks can overcome such attacks from the intruders and can offermore reliability in traffic sign detection for autonomous vehicles.Capsulenetwork have achieved the state-of-the-art accuracy of 97.6% on German TrafficSign Recognition Benchmark dataset (GTSRB).

Novel Deep Learning Model for Traffic Sign Detection Using Capsule Networks