Traffic Sign Recognition Algorithm Based on Siamese Neural Network with Encoder.

Traffic sign recognition has been applied to the assistant driving system. However, some factors, such as occlusion, contamination damage and weather can seriously affect the accuracy and robustness of traffic sign recognition function. To solve this problem, a traffic sign encoding and classification method based on the Siamese neural network is developed. The method treats the traffic sign recognition problem as a convolutional feature code recognition problem. Firstly, the method uses convolutional neural network to extract and encode features of training samples and reference samples. Secondly, the method uses Siamese neural network to compare the feature code of training samples and reference samples and trains the encoder with contrastive loss. With the help of a fully connected layer, the method can recombine and classify the convolutional feature code of input in the end. The experimental results show that this method can produce effective and robust feature code of traffic sign under motion blur and occlusion conditions. Compared to other advanced methods, this method has higher accuracy. [ABSTRACT FROM AUTHOR]