Aircraft landmark detection (ALD) aims at detecting the keypoints of aircraft, which can serve as an important role for subsequent applications such as fine-grained aircraft recognition. In ALD, the physical size discrepancy between different kinds of aircraft may lead to inconsistent landmark structure, which significantly harms landmark detection results. In this letter, we take advantage of the category prior to alleviate the size discrepancy in ALD. The proposed category-aware landmark detection network (CALDN) possesses two streams: a classification stream for size categorization and a localization stream for landmark detection. Instance-level size category information captured by classification stream serves as the guidance in the localization stream for robust landmark detection. Moreover, a category attention module (CAM) is proposed for better-utilizing category information to guide ALD. Benefitting from the adaptive attention mechanism, CAM can automatically highlight category-specific features for ulteriorly reducing the influence of size discrepancy. Furthermore, to advance ALD research, we contribute the first perspective-variant aircraft landmark dataset. Solid experiments demonstrate the superiority of our method.

中文翻译：

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类别感知飞机地标检测

飞机地标检测（ALD）旨在检测飞机的关键点，这可以作为后续应用的重要作用，如细粒度飞机识别。在 ALD 中，不同类型飞机之间的物理尺寸差异可能导致地标结构不一致，这会严重损害地标检测结果。在这封信中，我们利用类别之前减轻 ALD 中的大小差异。提出的类别感知地标检测网络（CALDN）具有两个流：用于大小分类的分类流和用于地标检测的定位流。分类流捕获的实例级大小类别信息作为定位流中的指导，用于鲁棒地标检测。而且，提出了类别注意模块（CAM）以更好地利用类别信息来指导 ALD。受益于自适应注意力机制，CAM 可以自动突出特定类别的特征，从而最终减少尺寸差异的影响。此外，为了推进 ALD 研究，我们贡献了第一个透视变体飞机地标数据集。坚实的实验证明了我们方法的优越性。