With the rapid development of smart stations and cloud computing, the deployment of large-scale video surveillance systems for pedestrian detection in subway stations is becoming more and more important, which plays an important role in passenger flow monitoring, passenger guidance, and behavior warning. In practical engineering applications, a lightweight pedestrian detection algorithm MCA-YOLOv5s is proposed due to the adverse effects of limited computing resources and difficult samples caused by multi-scale and multi-angle occlusion. Firstly, MobileNetv3 replaces the YOLOv5 backbone network to achieve lightweight network model processing, and PConv replaces DWConv in the MobileNetv3 network to reduce redundant computation and memory access. Secondly, the coordinate attention mechanism is incorporated in the C3 module of the feature fusion stage to make the model pay more attention to pedestrian position information. At the same time, the loss function CIoU is replaced by Alpha IoU to increase the weight of the High Loss target and the regression accuracy of the bounding box. Finally, the improved network model is compressed by FPGM pruning to improve the loading and running speed of the model. The improved model is deployed in Huawei Atlas 300 AI accelerator to detect pedestrians in subway stations. The average accuracy is 94.1%, and the detection speed is 104.1 fps. The actual engineering practice shows that the detection speed of the improved algorithm is increased by 71.8%, saving the hardware deployment resources in the station and meeting the actual engineering needs of pedestrian monitoring and management in subway stations with large passenger flow.