Abstract:YOLOv7 is a better-performing model in the current target detection task. However, when dealing with road intersections in remote sensing images, the problems of complex target backgrounds, significant positioning errors in the first frame, and the number of model training parameters increase. This paper proposes a road intersection detection model for remote sensing images that combines normalized Gaussian Wasserstein distance with lightweight YOLOv7 for road intersections in complex scenarios. Firstly, the transcendental box localization loss function is improved by normalized Gauss Wasserstein distance and CIOU to enhance the robustness of the network model to target size. Secondly, a three-dimensional attention mechanism is added to the enhanced network feature extraction module to achieve feature optimization. Finally, an improved FasterNet module is added to the backbone feature extraction network and enhanced feature extraction network to improve the network model's training speed and reduce the model training parameters. The results show that the improved YOLOv7 network model showed significant improvement in leakage detection compared to the original network model, with P, R, AP, and F1 values increasing by 6.2%, 4.9%, 6.7%, and 6.5%, respectively. It is concluded that solid adaptability to different environmental images provides a reference for developing road intersection detection.