Oriented RepPoints for Aerial Object Detection

Abstract

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.

Scattering-Point-Guided Oriented RepPoints for Ship Detection

“…G-Rep [35] devises a unified Gaussian representation to construct Gaussian distributions for both OBBs and PointSets, accompanied by a Gaussian regression loss to further enhance object detection performance. Oriented Reppoints [36] utilizes an adaptive point learning methodology to capture the geometric information of arbitrary orientation instances and formulate schemes for adaptive point quality assessment and sample allocation.…”

“…Furthermore, PAA [42] adapts sample allocation in a probabilistic manner. APAA [36] addresses the limitations of IOU in directional scenes by proposing an adaptive sample point set allocation scheme based on a comprehensive evaluation of orientation, classification, localization, and pixel-wise correlation. Although the above algorithms have proven their effectiveness in the field of optical images, in the field of SAR, we still need to further explore methods tailored to the characteristics of SAR images.…”

“…We improve the quality metric for adaptive sample reppoints, which is different from APAA [36], and we introduce a scattering position metric in the quality assessment to more comprehensively measure the quality of the sample reppoints and provide higher-quality samples for model training.…”

Orientation-First Strategy With Angle Attention Module for Rotated Object Detection in Remote Sensing Images

“…This type of method locates objects by detecting the key points, and then predicts other information at the positions of these key points. Subsequently, the application of anchor-free for object detection in RSIs has been extensively studied [38][39][40][41][42][43].…”

LUAI Challenge 2021 on Learning to Understand Aerial Images

“…3rd Place. The HIK HOW, team of Kaixuan Hu, Yingjia Bu, Wenming Tan from Hikvision Research Institute, apply Oriented Reppoints [16] and ROI Transformer [6] as baseline-detectors. They only use the competition dataset for training using pre-trained models from ImageNet, and no extra data was added in training.…”