Classification Matters More: Global Instance Contrast for Fine-Grained SAR Aircraft Detection

Since significant intraclass differences and inconspicuous interclass variations, fine-grained aircraft detection in synthetic aperture radar (SAR) images is challenging. Also, the inherent lack of detailed features and severe noise interference in SAR images make it difficult to learn class-specific feature representations. Current detection approaches focus more on localization accuracy and ignore classification performance, which is more critical in fine-grained detection. To address the above challenges, we present GICNet: global instance contrast (GIC) for fine-grained SAR aircraft detection a global instance-level contrast module is proposed to improve interclass divergences and intraclass compactness. With a specially constructed global instance set, GICNet can contrast a large number of different aircraft targets while keeping a small batch size. Furthermore, we design a novel quality-aware focal loss (QAFL) to facilitate the accurate classification of well-localized aircraft targets. Meanwhile, to maintain localization performance, we develop a new edge-aware bounding-box refinement (EABR) module to refine predicted coarse bounding boxes. Experimental results show that our GICNet outperforms current advanced detectors and achieves a new state-of-the-art performance on the GaoFen-3 SAR aircraft detection dataset. In particular, GICNet also has advantages in reducing misclassification and recognizing well-located targets.