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One of the challenges in problems related to road understanding is to deal with noisy images. Especially, recent studies have revealed that ConvNets are sensitive to small perturbations in the input. One solution for dealing with this problem is to generate many noisy images during training a ConvNet. However, this approach is very costly and it is not a certain solution. In this paper, we propose an objective function regularized by the local Lipschitz constant and train a ReLU layer for restoring noisy images. Our experiments on the GTSRB and the Caltech-Pedestrian datasets show that this lightweight approach not only increases the accuracy of the classification ConvNets on the clean datasets but it also increases the stability of the ConvNets against noise. Comparing our method with similar approaches shows that it produces more stable ConvNets while it is computationally similar or more efficient than these methods.

Increasing the Stability of CNNs using a Denoising Layer Regularized by Local Lipschitz Constant in Road Understanding Problems