Pedestrian targets in subway scenes pose problems such as varying sizes, different degrees of occlusion, and blurred images caused by dark environments, which adversely affect the accuracy of pedestrian target detection. To address these problems, this study proposes an improved YOLOv5s target detection algorithm to improve the accuracy of pedestrian target detection in subway scene video signals. The pedestrian dataset of a subway scene is constructed, the corresponding labels are marked, and the data preprocessing operation is performed. Moreover, a deep residual shrinkage network is added to the feature extraction module, and the residual network, attention mechanism, and soft thresholding function are combined to enhance the useful feature channel and weaken the redundant feature channel. The fusion features of multiscale and multireceptive fields of the image are obtained using the improved atrous spatial pyramid pooling module without losing image information, and the global context information of the image is effectively captured. The improved non-maximum suppression algorithm is designed to postprocess the target prediction frame and retain the optimal prediction frame of the detection target. The experimental results demonstrate that the improved YOLOv5s algorithm proposed in this study can effectively improve the accuracy of pedestrian target detection in subway scene video signals, particularly for small and dense pedestrian target scenes.