DRPDDet: Dynamic Rotated Proposals Decoder for Oriented Object Detection

Oriented object detection has gained popularity in diverse fields. However, in the domain of two-stage detection algorithms, the generation of high-quality proposals with a high recall rate remains a formidable challenge, especially in the context of remote sensing images where sparse and dense scenes coexist. To address this, we propose the DRPDDet method, which aims to improve the accuracy and recall of proposals for Oriented target detection. Our approach involves generating high-quality horizontal proposals and dynamically decoding them into rotated proposals to predict the final rotated bounding boxes. To achieve high-quality horizontal proposals, we introduce the innovative HarmonyRPN module. This module integrates foreground information from the RPN classification branch into the original feature map, creating a fused feature map that incorporates multi-scale foreground information. By doing so, the RPN generates horizontal proposals that focus more on foreground objects, which leads to improved regression performance. Additionally, we design a dynamic rotated proposals decoder that adaptively generates rotated proposals based on the constraints of the horizontal proposals, enabling accurate detection in complex scenes. We evaluate our proposed method on the DOTA and HRSC2016 remote sensing datasets, and the experimental results demonstrate its effectiveness in complex scenes. Our method improves the accuracy of proposals in various scenarios while maintaining a high recall rate.