Abstract: Aiming at the problem of missed detection and false detection due to high overlap rate of detection targets in traffic congested scenes, a combination optimization algorithm (named L-YOLOv3+CIoU Loss+SD-NMS) containing improved YOLOv3, CIoU loss function optimization, and SD-NMS optimization is proposed. Deep separable convolution, SE module, and Ghost module are used to improve the residual unit structure of YOLOv3, in order to improve the ability of dense target feature extraction and reduce the amount of network model parameters. CIoU Loss is adopted to speed up the network  model convergence speed. Meanwhile, the multi-target set prediction idea is combined with DIoU-NMS to propose the SD-NMS optimization algorithm, in order to reduce the false detection rate of missed detection. Experimental results on the BDD100K data set show that the improved target detection algorithm has a recall rate of 91.58% and an accuracy rate of 93.04%. Compared with YOLOv3 algorithm, the proposal improves the recall rate and accuracy by 12.09% and 9.52%, respectively, showing better detection effect. 

Key words: target detection, deep learning, YOLOv3 algorithm, CIoU loss, non-maximum suppression