基于多感知融合的遥感影像检测算法OA

To deal with the problem of complex background and small target detection in remote sensing images,a multi-sensory fusion-based detection algorithm YOLO-GT is proposed.In order to improve the feature information of small targets in the feature map,a detection head Adaptive Scale-Aware Dynamic Head(ASADH)which contains three kinds of sensing mechanisms is designed;at the same time,a lightweight up-sampling operator Content-Aware ReAssembly of Features(CARAFE)is introduced to solve the problem of semantic information loss and improve the performance of the feature pyramid network;then Wise-IoU is adopted as the loss function in order to further optimize the training speed and localization accuracy of the model.The experimental results on the DIOR dataset show that the model accuracy reaches 90.4％,which is 2.1％higher than the original algorithm.These improvements effectively enhance the performance of small targets detection in remote sensing images under complex backgrounds.