FFRViT: Frequency Feature Refinement Vision Transformer for Remote Sensing Object Detection

Rotated object detection in remote sensing images is a research hotspot in the field of computer vision. The following problems exist in the detection of rotating object in remote sensing images: Insufficient feature extraction ability, complex scenes lead to interference and occlusion between objects, and object characteristics are sensitive to positioning. To solve the above problems, we propose the Frequency Feature Refinement Vision Transformer (FFRViT). First, the initial feature extraction is performed on the input image. Then, the frequency division feature refinement of local, global, and contextual features is performed in four stages. This process not only emphasizes the key positional information of the image but also models global relationships and extracts surrounding environmental information as a supplement, thereby enhancing the model’s ability to capture comprehensive features. Finally, the detection head is used for object classification and localization. A sample collaborative optimization loss is designed to adaptively adjust the model’s emphasis on positive and negative samples, so as to further improve the detection accuracy of the model. Experiments on the HRSC2016 and DOTA-v1.0 public datasets show that the proposed method achieves better performance in rotated object detection in remote sensing images, and its detection accuracy is better than some mainstream methods.