A multi-scale YOLOv3 detection algorithm of road scene object

In natural traffic scene, the bounding box sizes of different road targets vary greatly. The existing real-time object detection algorithm YOLOv3 can not balance the detection accuracy of large and small targets and has poor performance in the task. To solve the problems, the feature fusion module of YOLOv3 target detection algorithm was redesigned to realize the multi-scale feature stitching. The detection module was improved by adding two extra feature output modules for small targets, and a new multi-scale detection method of YOLOv3₅ d for road targets was obtained with 5 detection scales. The experimental results show that the average precision of the improved YOLOv3₅ d algorithm is 0.580 9 on BDD100 K dataset, which is 0.082 0 higher than that of original YOLOv3. The running speed is 45.4 frames·s^-1, which can meet the real-time requirement.