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.