Oriented Target Detection in Remote Sensing Images Based on Multi-Scale Feature Fusion and Feature Compensation

The oriented target detection algorithm based on deep learning has made significant progress and has been widely applied in various fields, including remote sensing. However, existing methods still face challenges in large-sized targets and targets with similar backgrounds, leading to unsatisfactory detection performance in these scenarios. To address these issues, this paper proposes two modules on the basis of the feature pyramid: the Multi-Scale Feature Fusion Module and the Feature Compensation Module. The Multi-Scale Feature Fusion Module effectively integrates features from different levels, filters out noise introduced during the fusion process, and allows the network to focus more on target regions. The Feature Compensation Module provides semantic information compensation for the highest-level feature map, enhancing the feature representation capability. Extensive experiments were conducted on the DOTA and DIOR-R datasets. The experimental results demonstrate that the introduction of these two modules significantly improves the detection accuracy of the baseline algorithm.