【摘要】 针对交通拥堵的车辆密集场景中检测目标重叠率高而导致漏检和误检的问题，提出了改进YOLOv3、CIoU损失函数优化以及SD-NMS优化算法(简记L-YOLOv3+CIoU Loss+SD-NMS)。利用深度可分离卷积、SE模块和Ghost模块改进YOLOv3的残差单元结构，以提高对密集目标的特征提取能力，减少网络模型参数量；采用完整交并比CIoU损失函数加快网络模型收敛速度，同时将多目标集合预测思想与DIoU-NMS有机结合，提出了SD-NMS优化算法，以降低漏检误检率。在BDD100K数据集上进行实验，结果表明，改进的目标检测算法召回率达到91.58%,精准率达到93.04%,与YOLOv3算法相比，召回率和精准率分别提升了12.09%和9.52%,具有更好的检测效果。更多还原

【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.更多还原