关键词: 非机动车检测, YOLOv3, 特征融合, GIOU损失

Abstract:

With the strengthening of the supervision of non-motor vehicles by the traffic management department, the detection and identification of non-motor vehicles in the road traffic monitoring video will gradually become an essential function of the electronic traffic police system. Due to the high density of non-motor vehicles, it is easy to block each other, and the area occupied is often small in the surveillance video, which is prone to problems such as inaccurate detection and missed detection. Aiming at the problems of inaccuracy and omission of non-motor vehicle detection, based on YOLOv3（You Only Look Once）, an improved detection and recognition model for non-motor vehicles is proposed. By designing a new feature fusion structure, the detection rate of non-motor vehicles can be reduced, and the location accuracy can be improved by using GIOU（Generalized IOU） loss. The improved model obtains better detection results than YOLOv3 on the real complex scene non-motor vehicle data set, and the mean Average Precision（mAP） of detection is improved by 3.6%.

Key words: non-motor vehicle detection, You Only Look Once（YOLOv3）, feature fusion, Generalized IOU（GIOU） loss