Traffic Sign Detection for Smart Public Transport Vehicles: Cascading Convolutional Autoencoder With Convolutional Neural Network

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Traffic sign detection is one of the most important tasks for autonomous public transport vehicles. It provides a global view of the traffic signs on the road. In this chapter, we introduce a traffic sign detection method based on auto-encoders and Convolutional Neural Networks. For this purpose, we propose an end-to-end unsupervised/supervised learning method to solve a traffic sign detection task. The main idea of the proposed approach aims to perform an interconnection between an auto-encoder and a Convolutional Neural Networks to act as a single network to detect traffic signs under real-world conditions. The auto-encoder enhances the resolution of the input images and the convolutional neural network was used to detect and identify traffic signs. Besides, to build a traffic signs detector with high performance, we proposed a new traffic sign dataset. It contains more classes than the existing ones, which contain 10000 images from 73 traffic sign classes captured on the Chinese roads. The proposed detector proved its efficiency when evaluated on the custom dataset by achieving a mean average precision of 86.42%.