Research on subway pedestrian detection algorithms based on SSD model

Abstract

Accurate target recognition and location is one of the key technologies in the field of smart city application. In order to solve the problem of large pedestriain flow impact in crowded metro stations, a method of in‐depth learning detection based on SSD (single shot multibox detector) is proposed. The algorithm extracts the feature information of the input image, then returns the boundary box of the location on the feature map and classifies the object categories. Using the method of local feature extraction, the features of different positions, different aspect ratios and sizes are obtained, and VGG16 is used as the base network to optimise and improve the network structure. The results of simulation experiments on VOC2007 and data_sub show that the maximum value of mAP is 77% and the highest accuracy is 96.31%. Compared with other mainstream deep learning target detection methods, SSD has higher accuracy, better real‐time and robustness. It can solve the problem of different pedestrian target sizes and better realise pedestrians in subway station environment. Detection provides decision‐making basis for flow statistics.

Keywords

• subway pedestrian detection algorithms
• SSD model
• target recognition
• smart city application
• pedestriain flow impact
• crowded metro stations