Traffic Sign Detection Algorithm Based on Improved YOLOv5
Aiming at the problems of low accuracy and slow detection speed in identifying small traffic signs on roads in different environments during driverless driving,a target recognition algorithm based on improved YOLOv5 was proposed.In order to reduce the model volume and improve the model inference speed,this algorithm uses the Ghost fea-ture module to replace the original network architecture.Fusion and channel attention mechanisms are combined at the feature module level to help the model better focus on key information in the image.In order to enhance the feature ex-traction ability of small targets,the original C3 module was improved,the STB module(Swin Transformer Blocks)was in-troduced,and the Chinese traffic sign data set TT100K was introduced for comparison and verification.The results showed that the recognition accuracy of the method proposed in the paper was 92.6%,an increase of 0.7%;The value reached 93.5%,an increase of 2.5%.
NETL
NSTL
万方数据
维普
