Gaussian Wasserstein Distance Improvement of Lightweight YOLOv7 Model for Remote Sensing Image Road Intersection Detection
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.A road intersection detection model for remote sensing images that combines normalized Gaussian Wasserstein distance with lightweight YOLOv7 was proposed for road intersections in complex scenarios.Firstly,the transcendental box localization loss function was improved by normalized Gaussian Wasserstein distance and complete-IoU(CIoU)to enhance the robust-ness of the network model to target size.Secondly,a three-dimensional attention mechanism was added to the enhanced network feature extraction module to achieve feature optimization.Finally,an improved FasterNet module was 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 parame-ters.The results show that the improved YOLOv7 network model shows significant improvement in leakage detection compared to the original network model,with precision(P),recall(R),average precision(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.
万方数据
维普
