Robustness Analysis of Traffic Sign Recognization based on ResNet

Abstract

Autonomous driving has always been an important research topic and application task of artificial intelligence, which has attracted the attention of a large number of researchers. As an important component of the environmental perception module in autonomous driving tasks, traffic sign recognition can help drivers understand road information in a timely manner and avoid potentially dangerous driving operations. To this end, accurate recognition of traffic signs is crucial from both a strategic and a practical point of view. The early recognition technology of traffic signs is mainly based on the detection of color and shape, whose recognition accuracy is limited due to the fading and deformation of traffic signs. Numerous researchers have successfully used these deep learning-based object identification algorithms for traffic sign detection and recognition thanks to the development of the Faster R-CNN and YOLO series algorithms. However, we argue that few efforts focus on the recognition performance of the model in different scenarios, which is especially important in the process of autonomous driving. Based on this observation, this paper first constructs the ResNet model. Part of the image recognition accuracy predicted by the model reached 99%. Afterward, the robustness of the model is explored by simulating complex scenes by changing illumination and noise, and it is proved that the model has good generalization ability and practical application ability.