S2CGNet: A Robust Aircraft Detector Based on the Sword-Shaped Component Geometry
Chenglong Liu, Hongfeng Yu, Haoran Wei, Xian Sun, Kun Fu · IEEE Transactions on Geoscience and Remote Sensing · 2023
Aircraft detection is a challenging task for remote sensing images. The anchor-based methods are of high complexity, and the keypoint-based detectors suffer the grouping difficulty. Some line-based models relying on local features are hindered by adhesion and disintegrity problems. Moreover, those detection representations rarely take into account the sword-shaped component geometric semantics (e.g., fuselage and the wing) of the aircraft itself, leading to being less robust and unfavorable for downstream tasks, such as ones needing the detailed size and shape of aircraft. Accordingly, we model the sword-shaped component geometry and propose S2CGNet, a more robust appearance-based aircraft detector. The sword attenuation mask (SAM) module is devised to encode a “sword-shaped mask” for each aircraft while exploring more robustness via the geometric surface embedding. The SAM can provide clearer borders to separate different aircraft more precisely. Besides, to address the instance disintegrity problem and further boost the quality of SAM, we propose an instance aware graph (IAG) module to jointly optimize the parameters of the fuselage/wing detection heads. Experimental results show that the performance of S2CGNet can reach the state-of-the-art (SOTA) level. Specifically, it achieves 98.5% in terms of AP50 on the combined dataset of Aircraft-KP and NWPU VHR-10, boosting 3.8% than the baseline. Besides, S2CGNet boosts the quality of detection results greatly, e.g., it yields a significant improvement of 21.3% on AP75 compared to the baseline. Furthermore, the generalization comparisons on the FAIR1M dataset strongly demonstrate the robustness of our model surpasses other oriented detectors by a large margin.