Shuaiying Yan

Abstract Optical remote sensing object detection is challenged by sparse object distribution, large spatial coverage, and pronounced scale variation, which complicate effective feature representation and contextual reasoning. Transformer-based detectors enable global dependency modeling but often introduce redundant feature interactions and insufficient feature regulation during multi-scale fusion, limiting discriminative representation, particularly for small and low-contrast targets.