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An omni-directional gated spatial attention network (OGSA-YOLO) is proposed, which establishes a cohesive and unified architecture to systematically decouple multi-scale feature variations and suppress non-structural environmental noise.
Experimental results on public benchmark datasets demonstrate that the proposed SMDS-Net achieves highly competitive detection accuracy while significantly reducing the number of model parameters and theoretical computational complexity.
Instance segmentation of remote sensing images enables simultaneous target differentiation and pixel-level classification, boasting broad applications in urban planning, military reconnaissance, and marine monitoring. However, existing methods often exhibit strong label dependency, and acquiring large-scale, fine-grain...
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