LAUSR

With the development of deep convolutional neural networks, detecting rotating objects in remote-sensing images is of great significance in various fields. Existing rotating object detectors most suffer the problem of ambiguous supervision caused by inappropriate rotating object representations. This problem may result in fuzzy object localization and further lead to misclassification. In this article, we propose an Automatic Organized Points Detector (AOPDet), which derives precise localization results by applying a novel rotating object representation called nonsequential corners representation. To achieve the proposed representation, an Automatic Organization Mechanism (AOM) technique is designed to guide the model to organize points to object corners automatically. An Automatic-Organized-Points-specific (AOP-specific) head structure is also designed and equipped in the model to better focus on the rotating object detection task. On public aerial datasets, experiments show that the AOPDet achieves 17.0 mAP higher than the compared baseline model, reaching the state-of-the-art (SOTA) level. Detailed ablation experiments and error analysis strongly reveal the effectiveness of the proposed model.