A Method of Detect Traffic Police in Complex Scenes

Target detection has a wide range of applications in many areas of life, and it is also a research hotspot in the field of unmanned driving. Urban roads are complex and changeable, especially at intersections, which have always been a difficult and key part in the research of pilotless technology. Traffic policemen detection at intersections is a key link, but there are few existing algorithms, and the detection speed is generally slow. Aiming at this problem, this paper proposes a real-time detection method of traffic police based on YOLOv3 network.The YOLO network is robust and capable of quickly completing target detection tasks. According to the information investigated, there are currently few data sets on traffic police detection. In response to this problem, this paper adopts the transfer learning method, adopts the imageNet set to training model, learns the basic characteristics of people, and then selects 1000 pictures containing traffic police to conduct experiments. The average accuracy of traffic police detection is 77%, and the detection speed reaches 50FPS, which basically meets the requirements of real-time performance, indicating that the method is reasonable and feasible.