GAENet:复杂背景下遥感图像的舰船旋转目标检测网络OA

Achieving high-precision detection of rotated ship targets under complex background conditions remains a critical technical challenge in maritime target recognition within the field of computer vision.Affected by factors such as multi-scale target variations,severe background interference,dense target distributions,and arbitrary orientations,there is an urgent need to design ship-oriented detection algorithms with strong robustness and high discriminative capability to improve detection performance and generalization in real-world scenarios.In this work,an Adaptive Channel Attention Mechanism(ACAM)is introduced,which enables the network to automatically focus on regions and targets that are more critical for classification and localization tasks,thereby effectively integrating global and local information of ship targets in remote sensing images.Meanwhile,a lightweight Grouped Efficient Feature Fusion(GEFF)module is designed and combined with a novel U-shaped Feature Pyramid Network(U-FPN),allowing the fusion process to fully exploit the global and local receptive field information provided by ACAM.Furthermore,a Shared Task Dynamic Alignment Detection Head(STADH)is proposed to enhance the collaborative optimization between classification and regression tasks.Experimental results on the SCOSS and HRSC2016 datasets demonstrate that,compared with conventional methods,the proposed approach improves the mean Average Precision(mAP)by 1.4%and 1.1%,respectively,while reducing the number of parameters by 40%and the computational cost by 15.6%.