Target Detection in Remote Sensing Image Based on Deformable Transformer and Adaptive Detection Head

To address the challenges of precise localization of targets in optical remote sensing images and conflict between classification and localization features in the detection head, a remote sensing image target detection method based on Deformable Transformer and adaptive detection head is proposed. First, we design a feature extraction network based on feature fusion and Deformable Transformer. The feature fusion module enriches the semantic information of shallow convolution neural network features, and the Deformable Transformer establishes dependencies on distant features. This in turn effectively captures global semantic information and improves feature representation capability. Second, an adaptive detection head based on task learning module is constructed to enhance task awareness within the detection head. It automatically learns and adjusts the feature representation for classification and localization tasks, and thereby, mitigates feature conflicts. Finally, the L1-IoU loss is proposed as a localization loss function to provide a more accurate assessment of localization error between candidate boxes and ground truth boxes during training, thereby improving the accuracy of object localization. The effectiveness of the proposed method is evaluated on high-resolution remote sensing datasets, NWPU VHR-10 and RSOD. The results show significant improvements when compared to other methods.