In order to solve the problems of low detection accuracy under low light, insufficient spatial position accuracy and small target detection accuracy of existing deep learning algorithms, a deep learning method SSS-YOLO to improve the detection of foreign objects in subway cracks was proposed to improve YOLOv10 for the detection of foreign objects in subway cracks. In order to improve the image quality in the dark environment of subway gaps, considering the weights of some features at different scales, the parameter-free attention mechanism is introduced into the SSS-YOLO model, and the spatial position information is strengthened to reduce the amount of information loss, and finally the Shape-IOU loss function is used to enhance the accuracy of small target detection and regression prediction frame, and improve the detection accuracy of small and small targets in the gap. The experimental results show that the accuracy of the method reaches 90.90%, and the average detection accuracy is increased by 3.62%.