PolarDet: a fast, more precise detector for rotated target in aerial images

作者：Pengbo Zhao, Zhenshen Qu, Yingjia Bu, Wenming Tan, Qiuyu Guan · 发表于：International Journal of Remote Sensing · 年份：2021 · DOI：10.1080/01431161.2021.1931535 · 被引用次数：99 · 研究领域：Advanced Neural Network Applications、Advanced Image and Video Retrieval Techniques、Infrared Target Detection Methodologies

Fast and precise object detection for hgigh-resolution aerial images has been a challenging task over the years. Due to the sharp variations in object scale, rotation, and aspect ratio, most existing methods are inefficient and imprecise. In this paper, we propose a different approach polar method. We locate an object by centre-point, direct it by four polar angles, and measure it by polar ratio system. Our polar coordinate-based method, PolarDet, is a faster, simpler, and more accurate one-stage object detector. Also, our detector introduces a sub-pixel centre semantic structure to further improve classifying veracity. PolarDet achieves nearly all state-of-the-art (SOTA) performance in aerial object detection tasks with faster inference speed. In detail, our approach obtains the SOTA results on authoritative remote sensing object detection datasets DOTA, UCAS-AOD, and HRSC2016 with 76.64% mAP (mean average precision), 97.01% mAP, and 90.46% mAP respectively. Most noticeably, our PolarDet gets the best performance and reaches the fastest speed (32fps) at the UCAS-AOD dataset.