基于多尺度加权特征融合网络的地铁行人目标检测算法

随着地铁乘客的大量增加，实时准确地监测地铁站内客流量对于保证乘客安全具有重要意义。针对地铁场景复杂、行人目标小等特点，该文提出了多尺度加权特征融合(MWF)网络，实现地铁客流量的精准实时监测。在数据预处理阶段，该文提出过采样目标增强算法，对小目标占比不足的图片进行拼接处理，增加小目标在训练时的迭代频率。其次，在单镜头多核检测器(SSD)网络基础上添加了基于VGG16网络的特征提取层，将不同尺度的特征层以不同方式进行加权融合，并选出最优的特征融合方式。最终，结合小目标过采样增强算法，得到多尺度加权特征融合模型。实验证明，该方法与SSD网络相比，在保证实时性的同时，检测精度提升了5.82%。

Metro Pedestrian Detection Algorithm Based on Multi-scale Weighted Feature Fusion Network

With the large increase of passengers in metro stations, precise and real-time monitoring of passenger flow in subway stations is of great significance for ensuring passenger safety. Based on the features of complicated subway scenes and small pedestrian targets, a Multi-scale Weighted Feature (MWF) fusion network to achieve accurate real-time monitoring of subway passengers is proposed. In the data preprocessing stage, an oversampling target enhancement algorithm is proposed to stitch the pictures with an insufficient proportion of small targets to increase the iteration frequency of small targets during training. Secondly, feature extraction layers based on the VGG16 network are added to the Single Shot multibox Detector (SSD) network. The feature layers of different scales are weighted and fused in different ways, and the optimal feature fusion method is selected. Finally, combined with the small target oversampling enhancement algorithm, a multi-scale weighted feature fusion model is obtained. Experiments show that the detection accuracy of this method has improved by 5.82 percent compared with the SSD network and doesn’t reduce the speed of detection.