【摘要】 针对交通监控视频车辆检测常易受到遮挡导致目标车辆出现漏检或误检的问题,提出一种基于改进Mask R-CNN的交通监控视频车辆检测算法.采用基于bottleneck结构的主干网络,提高主干网络提取特征的能力;通过基于预测mask分数的掩码分支,融合目标的类别分数和掩码质量分数,提高车辆的掩码质量;通过基于Arcface Loss的目标检测损失函数设计,提高不同特征之间的可判别性,提高目标的检测精度.实验结果表明,改进的Mask R-CNN模型可更好地检测到被遮挡的车辆,目标车辆的检测精度超过Faster R-CNN、YOLO v3和Mask R-CNN模型,可解决目标车辆漏检或误检问题.更多还原

【Abstract】 Aiming at the problem of missing detection or wrong detection of target vehicles caused by occlusion in traffic surveillance video,an improved vehicle detection algorithm based on Mask R-CNN traffic surveillance video is proposed.Firstly,the backbone network based on the bottleneck structure is used to improve the ability of extracting features from the backbone network. Then,the mask branch based on the predicted mask score is used to fuse the target’s category score and mask quality score to improve the vehicle’s mask quality. Finally,the target detection loss function based on Arcface Loss can improve the discriminability between different features and improve the detection accuracy of the target.The experimental results show that the improved Mask R-CNN model can better detect the shielded vehicle,and that the detection accuracy of the target vehicle is higher than those of the Faster R-CNN,YOLO v3 and Mask R-CNN model,thus solving the problem of missing or wrong detection of the target vehicle.更多还原