Remote sensing target detection algorithm based on attention and feature alignment

To address the challenges of remote sensing images, including diverse target angles, intricate backgrounds, and overall clutter, we propose a remote sensing target detection method that utilizes attention mechanisms and feature alignment. First, a convolutional triplet attention mechanism is incorporated into the backbone network to improve the capture of contextual dependencies between adjacent regions in the feature map, thereby enhancing multi-scale feature learning from remote sensing images. Next, the detection head employs a rotational feature alignment network to create high-quality rotational anchors and aligned features for classification and coordinate regression. Finally, the Kernelized Focal IoU is used as the regression loss function for rotated bounding boxes, increasing the model's sensitivity to positioning accuracy and minimizing errors. The algorithm presented in this paper was evaluated on the large-scale remote sensing dataset DOTAv1.0, achieving an mAP of 0.786, and the experiments have demonstrated the effectiveness of the algorithm.