MFIViT: multiroute feature interaction 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 Multi-route Feature interaction Vision Transformer (MFIViT). 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. Finally, the detection head is used for object classification and localization. A sample collaborative optimization loss is designed to adjust the model's emphasis on positive and negative samples. Experiments on the DOTA-v1.0 public datasets show that the proposed method achieves better performance in rotated object detection in remote sensing images.