A real-time and lightweight traffic sign detection method based on ghost-YOLO

Traffic sign detection is an essential part of traffic security and unmanned driving system. Due to the changes in the traffic environment is complex, how to intelligently and efficiently detect traffic signs in real scenes is of great significance. The traffic sign detection task is characterized by many small targets and complex environmental interference, and the detection scene also requires the detection model to be lightweight and efficient. This paper proposes a lightweight model Ghost-YOLO, and a lightweight module C3Ghost is designed to replace the feature extraction module in YOLOv5. C3Ghost modules extract features in a lightweight way, which effectively speeds up inference. At the same time, a new multi-scale feature extraction is designed to enhance the focus on small targets. Experimental results show that the mAP of the Ghost-YOLO is 92.71%, and the number of parameters and computations are respectively reduced to 91.4% and 50.29% of the original. Compared with multiple lightweight models, the speed and accuracy of this method are competitive.