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LiM-YOLO improves ship detection in satellite imagery with fewer parameters

By PulseAugur Editorial · [1 sources] ·

Researchers have developed LiM-YOLO, a novel object detection model optimized for identifying ships in optical remote sensing imagery. The model addresses limitations in standard YOLO architectures by shifting the detection head to lower pyramid levels, improving the representation of small, high-aspect-ratio targets. LiM-YOLO also incorporates a group-normalized auxiliary projection module to enhance training stability on high-resolution satellite inputs. This streamlined detector achieves state-of-the-art performance with significantly fewer parameters than existing models. AI

IMPACT This research offers a more efficient and accurate method for object detection in satellite imagery, potentially improving surveillance and maritime monitoring capabilities.

RANK_REASON This is a research paper describing a new model and methodology for a specific computer vision task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LiM-YOLO improves ship detection in satellite imagery with fewer parameters

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This is a research paper describing a new model and methodology for a specific computer vision task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Seon-Hoon Kim, Yerin Kim, Hyeji Sim, Youeyun Jung, Okchul Jung, Daewon Chung ·

    LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing

    arXiv:2512.09700v3 Announce Type: replace Abstract: General-purpose object detectors face fundamental structural limitations when applied to ship detection in satellite imagery, where the ship scale distribution is concentrated at small sizes and high aspect ratios. In convention…

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