PolarDet: A Fast, More Precise Detector for Rotated Target in Aerial Images

Abstract

Fast and precise object detection for high-resolution aerial images has been a challenging task over the years. Due to the sharp variations on object scale, rotation, and aspect ratio, most existing methods are inefficient and imprecise. In this paper, we represent the oriented objects by polar method in polar coordinate and propose PolarDet, a fast and accurate one-stage object detector based on that representation. Our detector introduces a sub-pixel center semantic structure to further improve classifying veracity. PolarDet achieves nearly all SOTA performance in aerial object detection tasks with faster inference speed. In detail, our approach obtains the SOTA results on DOTA, UCAS-AOD, HRSC with 76.64\% mAP, 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.

arXiv comment11 pages, 10 figures, 5 tables

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Cited by in corpus (11)

• Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergence
• Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss
• G-Rep: Gaussian Representation for Arbitrary-Oriented Object Detection
• DARDet: A Dense Anchor-free Rotated Object Detector in Aerial Images
• Task-wise Sampling Convolutions for Arbitrary-Oriented Object Detection in Aerial Images
• Multi-Grained Angle Representation for Remote Sensing Object Detection
• Dense Label Encoding for Boundary Discontinuity Free Rotation Detection
• FCOSR: A Simple Anchor-free Rotated Detector for Aerial Object Detection
• Enhancing Rotated Object Detection via Anisotropic Gaussian Bounding Box and Bhattacharyya Distance
• Learning Polar Encodings for Arbitrary-Oriented Ship Detection in SAR Images
• OSKDet: Towards Orientation-sensitive Keypoint Localization for Rotated Object Detection