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 centrepoint, 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-ofthe-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.

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