基于大核选择和形状自适应的遥感图像目标检测OA

Target detection in optical remote sensing images is a key technology for the intelligent interpretation of remote sensing data.To address the challenges posed by of significant scale variations,background interfer-ence and the diversity of target shapes in remote sensing image detection,this paper proposes the Large Multi-scale Kernel(LMK)network.This network utilized large kernel convolution and a multi-scale attention mecha-nism to dynamically adjust the spatial receptive field,thereby enhancing the capture of contextual information about objects in remote sensing scenes.Furthermore,a Shape-Adaptive Selection(SAS)label allocation strat-egy was designed for target detection,which focused on the aspect ratio of the target shapes and calculated an optimal IoU threshold based on the shape information and feature distribution.To address the difficulty of target orientation and positioning in remote sensing images,this paper introduced the KFIoU loss function.Experi-mental results show that the proposed target detection model achieves accuracies of 96.73%,97.85%,and 77.26%on the HRSC 2016,UCAS-AOD,and DOTA datasets,respectively,outperforming most existing target detection algorithms.