Do Capsule Networks Solve the Problem of Rotation Invariance for Traffic Sign Classification?

Detecting and classifying traffic signs is a very import step to future autonomous driving. In contrast to earlier approaches with handcrafted features, modern neural networks learn the representation of the classes themselves. Current convolutional neural networks achieve very high accuracy when classifying images, but they have one big problem with their robustness to shift and rotation. In this work an evaluation of a new technique with Capsule Networks is performed and the results are compared to a standard Convolutional Neural Network and a Spatial Transformer Network. Moreover various methods for augmenting the training data are evaluated. This comparison shows the big advantages of the Capsule Networks but also their restrictions. They give a big boost in solving problems mentioned above but their computational complexity is much higher than convolutional neural networks.