Car, Cyclist and Pedestrian Object Detection Based on YOLOv5

Abstract

The target detection of car, cyclist and pedestrian is of great significance to the realization of automatic monitoring and artificial intelligence assisted driving system. In order to rapidly detect car, cyclist and pedestrian, You Only Look Once (YOLO) model is applied to car, cyclist and pedestrian detection tasks in this paper. Experimental result shows that among the four versions of YOLOv5, the mean average precision (MAP) is more than 91.7%, and YOLOv5x has the best recognition ability, which reaches 92.6%. Among the three categories, car recognition accuracy is the highest, followed by cyclist and pedestrian.