Arbitrary oriented object detection (AOOD) is a fundamental task in aeiral image interpretation, which is commonly implemented by optimizing three subtasks: classification, localization, and orientation. The consistency of classification, localization, and orientation is crucial for achieving high performance of AOOD detectors. However, independent prediction between subtasks and differences in quality metrics for task-specific samples reduces the accuracy of high-precision detection. In this paper, we propose a novel dense anchor-free detector rientation-aware daptive ampling earning (OASL) that includes two core modules: Orientation-aware Structured Information Extraction Module (O-SIEM) and Weights adaptive Sample Information Richness Metric (W-SIRM). Specifically, O-SIEM applies the global and local receptive fields of objects to explicitly extract spatial contextual features of oriented and densely distributed objects, which helps to strike a balance between capturing task-interactive and task-specific features, ultimately leading to learning common patterns between subtasks. W-SIRM integrates information from three subtasks and achieves dynamic evaluation of sample quality and adaptive adjustment of sample weights based on the information richness of samples. Extensive experiments demonstrate that the proposed OASL method can effectively handle the consistency problem and enhance the validity of the sample quality metric.

中文翻译：

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OASL：用于任意方向物体检测的方向感知自适应采样学习

任意定向目标检测（AOOD）是航空图像解释中的一项基本任务，通常通过优化三个子任务来实现：分类、定位和定向。分类、定位和定向的一致性对于实现 AOOD 探测器的高性能至关重要。然而，子任务之间的独立预测以及特定任务样本的质量指标差异降低了高精度检测的准确性。在本文中，我们提出了一种新颖的密集无锚检测器方向感知自适应放大收益（OASL），包括两个核心模块：方向感知结构化信息提取模块（O-SIEM）和权重自适应样本信息丰富度度量（W- SIRM）。具体来说，O-SIEM应用对象的全局和局部感受野来显式提取定向且密集分布的对象的空间上下文特征，这有助于在捕获任务交互特征和任务特定特征之间取得平衡，最终导致学习共同模式子任务之间。 W-SIRM整合了三个子任务的信息，实现了样本质量的动态评估和基于样本信息丰富程度的样本权重的自适应调整。大量实验表明，所提出的OASL方法可以有效处理一致性问题并增强样本质量度量的有效性。