Improved YOLOv5 for real-time traffic signs recognition in bad weather conditions

One of significant tasks in autonomous vehicle technology is traffic signs recognizing. It helps to avoid traffic violations on the road. However, recognition of traffic signs becomes more complicated in bad weather such as lack of light, rain, fog. Those bad weather conditions cause low accuracy of detecting and recognizing. In this paper, we aim to build a model to recognize and classify the traffic signs in different bad weather conditions by applying deep learning technique. Weather data are collected from variety types as well as generated from different techniques. Collected data are trained on the YOLOv5s, YOLOv7 model. In order to increase the accuracy, those YOLOv5s are improved on different models by replacing Squeeze-and-Excitation (SE) attention module or Global Context(GC) block. On the test set: the accuracy of YOLOv5s is 76.8%, the accuracy of YOLOv7 is 78% the accuracy of YOLOv5s+SE attention module is 78.4% and the accuracy of YOLOv5s+C3GC is 79.2%. The results show that YOLOv5s+C3GC model significantly improves the accuracy in recognition of blurred-distant-objects.