RS-DETR: An Improved DETR for High-Resolution Remote Sensing Image Object Detection

High-resolution remote sensing image object detection is an important research area in remote sensing information processing and has substantial practical applications. This domain presents unique challenges, including variable object scales, complex backgrounds, prevalent small objects, and densely arranged items, distinguishing it from traditional object detection in natural images. This paper proposes a novel object detection algorithm(RS-DETR), which builds upon the DETR framework and integrates the Swin Transformer. The algorithm features a dual-branch structure in its feature extraction module, markedly improving detection accuracy, especially for objects of varying scales. The addition of the GAM convolutional attention mechanism allows the model to concentrate more effectively on relevant regions, minimizing background complexities. Moreover, we have included the scale-invariant intersection over union (SIoU) loss function to enhance the precise localization of closely packed objects. To demonstrate the efficacy of the algorithm, RS-DETR was applied to the HRSC2016 and NWPU VHR-10 datasets. The results show average detection accuracies of 86.1% and 57.9% on these datasets, respectively, outperforming the baseline models by 1.1% and 0.9%, respectively.