Category-Aware Aircraft Landmark Detection

Abstract

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.