A context-aware transformer with weighted residual for efficient remote sensing target detection

Remote sensing image target detection has important applications in disaster prevention and mitigation. However, current detection models still have shortcomings in multi-scale feature fusion and in representing contextual information, and are prone to class imbalance during training. To address these issues, this paper proposes a target detection model that combines a context transformer and a weighted residual pyramid. Specifically, a weighted residual pyramid module is designed to fuse deep and shallow target-instance features effectively, and a learnable balancing factor is introduced to alleviate the imbalance in contributions across network layers. Simultaneously, a context transformer module is introduced into the feature extraction and fusion networks to enhance the model's multi-scale feature representation capability. Furthermore, a rotated bounding box is introduced to locate target instances, reducing the influence of redundant background information, and a CIoU-based multi-task loss function is designed to reduce the contribution of different instance targets to the regression task. Experimental results show that, while ensuring real-time performance, the proposed model achieves mAP@0.5 scores of 0.754 and 0.714 on the DOTAv1.0 and DOTAv1.5 datasets, respectively, demonstrating consistent improvements across both datasets. Meanwhile, visualization of detection results across various disaster scenarios shows that the model proposed in this paper has strong practical value.