多分支无锚框网络密集行人检测算法

Dense pedestrian detection algorithm in multi-branch non-anchor frame network

针对街道等多人流量场景图像中人员密集、姿态变化多、人体遮挡严重造成的行人检测漏检问题,提出一种多分支无锚框网络(MBAN)行人检测方法。首先,在检测模型主干网络后加入多分支网络结构用以检测行人的多个关键区域局部特征;然后,设计了关键区域之间的距离损失函数引导分支网络对行人的局部检测位置进行差异化学习,接下来为了提高分支网络对行人局部特征空间信息的理解能力,在Resnet50网络尾部加入四个上采样块构成沙漏结构(Hourglass);最后,设计了一种局部特征选择网络自适应抑制多分支输出的非最优值,消除预测时的冗余特征框。实验结果表明MBAN方法对多人流量场景行人检测的mAP值、F1值、Prec和Recall分别达到85.22%,0.87,80.07%和94.39%,证明该方法对密集人群检测能力较强,与其他行人检测算法相比有较高的召回率。

Considering the problem of missed pedestrian detection in dense pedestrian images,a multibranch non-anchor frame network(MBAN)detection method is proposed to detect various posture changes and serious human occlusion in multi-person traffic scenes,such as streets.First,a multi-branch network structure is added after model backbone network detection to detect the local features of multiple key areas with pedestrians.Subsequently,the distance loss function between key areas is designed to guide the branch network to differentially learn the local detection position of pedestrians.Thereafter,four up-sampling blocks are added to the tail of the ResNet50 network to form an hourglass structure,thereby improving the branch network’s ability to understand the spatial information of local features of pedestrians.Finally,a local feature selection network is designed to adaptively suppress the non-optimal values of the multi-branch output and eliminate the redundant feature box in prediction.In the experimental results,the mAP,F1,Prec,and Recall values of the MBAN method for pedestrian detection in multi-person scenes reached 85.22%,0.87,80.07%,and 94.39%,respectively.Therefore,this method is effective in detecting pedestrians in dense crowds and has higher recall rate compared with other pedestrian detection algorithms.