Unified Five-Distance Bounding Box Representation for Remote Sensing Oriented Object Detection
Yajun Qiao, Lingjuan Miao, Zhiqiang Zhou, Qi Ming, Yuhao Wang · IEEE Transactions on Geoscience and Remote Sensing · 2025
Currently, due to the densely packed and varied orientations of objects in the remote sensing images, the oriented object detection is still a challenging task. In the existing methods, the oriented bounding box (OBB) representations either include angle parameters or require a larger number of parameters and additional processes to obtain the final rotated rectangles, which can potentially induce the positional errors and decrease the accuracy of object detection in remote sensing images. In this paper, we propose a unified five-distance bounding box representation for remote sensing oriented object detection, which gets rid of the utilization of angle parameters and simultaneously represents the OBBs with the least distance-based parameters. We then construct a unified five-distance region proposal network (UF-RPN) based on the new OBB representation to achieve higher performance of oriented object detection. Additionally, most existing two-stage methods assume that the localization errors of objects are relatively small in the second stage of the network and believe that directly regressing the errors of general OBB representation would achieve sufficiently-high performance of object detection, neglecting that there is still room for improvement in the detection head by utilizing a more advanced OBB representation. Therefore, a five-distance detection head (FD-Head) is developed by utilizing the five-distance OBB representation in the detection head. Experimental results on the HRSC2016, DIOR-R, DOTA-v1.0 and DOTA-v2.0 datasets demonstrate the superiority and robustness of our method. Our code is publicly available at https://github.com/QYJ123/UFDet.