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.

In recent years, the emergence of convolutional neural networks (CNN) has greatly promoted the development of the object detection field, and many CNN-based detectors have achieved excellent performance on object detection in remote sensing images. To accurately locate the target, oriented bounding box (OBB) is usually used in remote sensing objects, such as the angle-based OBB, to represent the target. Nevertheless, the critical loss instability caused by the periodicity of the angle is always difficult to solve. In this paper, we propose a novel strategy called the Center-Guide points (CGP) box method that uses the guide points to locate the target, which breaks the limit of the angle-based thinking pattern to solve the critical loss instability problem. To be specific, we define a new guide-points selection rule and prediction structure, which replaces the traditional method of using angle values to indicate the direction. Furthermore, we propose the matching method of centre points and guide points, which is a box decoding method that matches the object and the corresponding guide points. Finally, an attention learning module called the Gaussian Center-Line (GC-L) Attention module based on the Gaussian centre-line is proposed to improve the accuracy of guide points. These strategies are applied to the key point detection framework and tested on three classical-oriented object remote sensing datasets. The results show that our method is effective and competitive.