AI RESEARCH
LiM-YOLO: Less is More with Pyramid Level Shift and Normalized Auxiliary Branch for Ship Detection in Optical Remote Sensing Imagery
arXiv CS.CV
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ArXi:2512.09700v2 Announce Type: replace Applying general-purpose object detectors to ship detection in satellite imagery presents significant challenges due to the extreme scale disparity and high aspect ratios of maritime targets. In conventional YOLO architectures, the deepest feature pyramid level (P5, stride of 32) compresses narrow vessels into sub-pixel representations, causing severe spatial feature dilution that prevents the network from resolving fine-grained ship boundaries.