High-Quality Angle Prediction for Oriented Object Detection in Remote Sensing Images

Oriented object detection is a challenging task in remote sensing, where the detected objects can be represented by oriented bounding boxes (OBBs). Angle prediction in oriented object detection has been widely studied, due to its crucial role in object detection. However, the precision of angle prediction is severely limited by misalignments in most of the existing methods, including representation-, evaluation-, and optimization-based misalignments. To alleviate these misalignments, this article presents a novel angle prediction method, called angle quality estimation (AQE). Specifically, our proposed AQE transforms the angle prediction task into a distribution estimation task to address the representation misalignment problem and implicitly measure the quality of the predicted angles. Based on the estimated AQ, we then propose a new metric to comprehensively evaluate the quality of OBBs. Then, we propose an object aspect ratio-based loss function to optimize angle prediction for addressing the optimization misalignment. Our proposed AQE is a plug-and-play method, which can be embedded on any existing oriented object detector. Experimental results on three public benchmarks, including dataset for object detection in aerial images (DOTA), high resolution ship collections 2016 (HRSC2016), and International Conference on Document Analysis and Recognition 2015 (ICDAR2015) datasets, show that our method achieves better performance than the other state of the arts.