Research on traffic sign image recognition and classification methods based on CNN

Abstract

To solve the problem of misclassification of traffic signs under various scale conditions, similar categories with insufficient illumination, image blurriness, partial occlusions, etc., a light-weight multi-scale attention convolutional neural network is introduced. Use a low-resolution image for edge extraction combined with color texture; add residual block layers at last step to increase depth of semantic information reconstruction. Multi-scale convolutional Branches are used to enhance the model's capability of recognizing distant Small Targets and scales changing sample. Introduce a Channel-pace Attention Module to enhance target detection and Tracking Performance in Key Regions. Focal Loss, label smoothing algorithms to improve the model at test time. The top-1 accuracy of experiment results from the German Traffic Sign Recognition Benchmark (GTSRB) was as high as 99.08%, and its F1 scores were around 98.97%. It has strong robustness under conditions such as poor lighting, raindrops, haze, occlusion, and faraway small targets, with moderate model size, computing power consumption, and inference speed. It is appropriate to apply this design in practice.