In contrast to the oriented bounding boxes, point set representation has great potential to capture the detailed structure of instances with the arbitrary orientations, large aspect ratios and dense distribution in aerial images. However, the conventional point set-based approaches are handcrafted with the fixed locations using points-to-points supervision, which hurts their flexibility on the fine-grained feature extraction. To address these limitations, in this paper, we propose a novel approach to aerial object detection, named Oriented RepPoints. Specifically, we suggest to employ a set of adaptive points to capture the geometric and spatial information of the arbitrary-oriented objects, which is able to automatically arrange themselves over the object in a spatial and semantic scenario. To facilitate the supervised learning, the oriented conversion function is proposed to explicitly map the adaptive point set into an oriented bounding box. Moreover, we introduce an effective quality assessment measure to select the point set samples for training, which can choose the representative items with respect to their potentials on orientated object detection. Furthermore, we suggest a spatial constraint to penalize the outlier points outside the ground-truth bounding box. In addition to the traditional evaluation metric mAP focusing on overlap ratio, we propose a new metric mAOE to measure the orientation accuracy that is usually neglected in the previous studies on oriented object detection. Experiments on three widely used datasets including DOTA, HRSC2016 and UCAS-AOD demonstrate that our proposed approach is effective.
翻译:与定向捆绑框相比,点定表示具有巨大的潜力,可以捕捉任意定向、大侧比和空中图像密集分布等详细实例结构,然而,常规点定方法是使用点对点监督固定地点手工制作的,这损害了这些地点在细微地貌提取上的灵活性。为了解决这些局限性,我们在本文件中提议了一种新颖的天体探测方法,名为Orient Reppoints。具体地说,我们建议使用一套适应点来捕捉任意定向物体的几何和空间信息,这些天体能够在空间和语义情景中自动安排自己对天体进行排列。为便利受监督的学习,建议定向转换功能将适应点明确映射成一个定向约束框。此外,我们引入有效的质量评估措施,选择设定点的训练样本,可以选择具有代表性的物体在定向物体探测方法上的潜力。此外,我们建议用空间限制来惩罚地面绑定物体外的外方点,在空间和语义情景中可以自动排列。此外,除了传统的IMAS 方向研究外,我们通常使用的新方向的路径定位矩阵测量方法,还显示以前的矩阵测量方法的精确度比。