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 pre-defined anchors with different scales, angles, and aspect ratios, and are optimized with a distance loss. Therefore, these methods are sensitive to anchor hyper-parameters and easily suffer from performance degradation caused by boundary discontinuity. To handle this problem, in this paper, 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 to extracts aligned features and introduce a 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 at https://github.com/zf020114/DARDet.

本文提出了一种基于密集无锚点的旋转目标检测器(DARDet)，直接预测特征映射中每个前景像素的旋转框的五个参数，同时设计了一个新的对齐卷积模块来提取对齐的特征，并引入 PIoU loss 进行精确和稳定的回归。在三个常用的航空目标数据集(DOTA，HRSC2016 和 UCAS-AOD) 上取得了最先进的性能，同时保持高效率。

DARDet：一种用于航拍图像中稠密无锚点旋转物体检测的算法