Traffic sign recognition algorithm and application for new energy vehicles based on artificial intelligence

This paper proposes an algorithm of traffic sign recognition based on double attention fusion. By using multiscale feature extraction module, this algorithm integrates spatial and channel attention mechanism to enhance key features, and cooperates with light weight classifier to reduce computation cost. Experiments were carried out on a German Traffic Sign Recognition Benchmark (GTSRB) and a Belgian Traffic Sign Dataset (BTS), using template matching algorithms such as SIFT, YOLO and SSD to compare. It is shown that the precision of GTSRB data set is 98.7%, 3.2% higher than ResNet, and F1 value reaches 98.5%; for complex scene of BTS data set, the recall rate reaches 94.5%, mean average accuracy (mAP) reaches 92.3%, so it is obviously superior to traditional algorithms and deep learning algorithm. The proposed algorithm keeps high recognition precision in rainy days, nights and other special scenes. The reasoning speed reaches 45 frames/second to meet the real time demand of new energy vehicles. This algorithm can effectively improve traffic sign recognition performance, provide reliable technology support for new energy vehicle intelligent driving.