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

LiM-YOLO 以更少参数提升卫星图像中的船舶检测能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-27 04:00

研究人员开发了 LiM-YOLO，这是一种新颖的目标检测模型，针对光学遥感图像中的船舶识别进行了优化。该模型通过将检测头移至较低的金字塔层来解决标准 YOLO 架构的局限性，从而改善了对小型、高纵横比目标的表示。LiM-YOLO 还包含一个组归一化的辅助投影模块，以增强高分辨率卫星输入上的训练稳定性。这种精简的检测器以显著少于现有模型的参数实现了最先进的性能。 AI

影响这项研究为卫星图像中的目标检测提供了一种更有效、更准确的方法，有可能提高监视和海上监控能力。

排序理由这是一篇描述特定计算机视觉任务新模型和方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Seon-Hoon Kim, Yerin Kim, Hyeji Sim, Youeyun Jung, Okchul Jung, Daewon Chung · 2026-05-27 04:00

LiM-YOLO：光学遥感船舶检测的“少即是多”金字塔层移位法

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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LiM-YOLO：光学遥感船舶检测的“少即是多”金字塔层移位法

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