DARDet: A Dense Anchor-Free Rotated Object Detector in Aerial Images
Feng Zhang, Xueying Wang, Shilin Zhou, Yingqian Wang · IEEE Geoscience and Remote Sensing Letters · 2021
Rotated object detection in aerial images has received increasing attention for a wide range of applications. However, it is also a challenging task due to the huge variations of scale, rotation, aspect ratio, and densely arranged targets. Most existing methods heavily rely on a large number of predefined anchors with different scales, angles, and aspect ratios, and are optimized with a distance loss. Therefore, these methods are sensitive to anchor hyperparameters and easily suffer from performance degradation caused by boundary discontinuity. To handle this problem, in this letter, we propose a dense anchor-free rotated object detector (DARDet) for rotated object detection in aerial images. Our DARDet directly predicts five parameters of rotated boxes at each foreground pixel of feature maps. We design a new alignment convolution module (ACM) to extract aligned features and introduce a pixels-intersection over union (PIoU) loss for precise and stable regression. Our method achieves state-of-the-art performance on three commonly used aerial objects datasets (i.e., DOTA, HRSC2016, and UCAS-AOD) while keeping high efficiency. Code is available athttps://github.com/zf020114/DARDet.