Applying general-purpose object detectors to ship detection in satellite imagery presents significant challenges due to the extreme scale disparity and morphological anisotropy of maritime targets. Standard architectures utilizing stride-32 (P5) layers often fail to resolve narrow vessels, resulting in spatial feature dilution. In this work, we propose LiM-YOLO, a specialized detector designed to resolve these domain-specific conflicts. Based on a statistical analysis of ship scales, we introduce a Pyramid Level Shift Strategy that reconfigures the detection head to P2-P4. This shift ensures compliance with Nyquist sampling criteria for small objects while eliminating the computational redundancy of deep layers. To further enhance training stability on high-resolution inputs, we incorporate a Group Normalized Convolutional Block for Linear Projection (GN-CBLinear), which mitigates gradient volatility in micro-batch settings. Validated on SODA-A, DOTA-v1.5, FAIR1M-v2.0, and ShipRSImageNet-V1, LiM-YOLO demonstrates superior detection accuracy and efficiency compared to state-of-the-art models. The code is available at https://github.com/egshkim/LiM-YOLO.

翻译：将通用目标检测器应用于卫星图像中的船舶检测面临重大挑战，这主要源于海上目标的极端尺度差异和形态各向异性。采用步长为32（P5）层的标准架构通常无法有效解析狭窄船舶，导致空间特征稀释。在本研究中，我们提出LiM-YOLO，一种专为解决这些领域特定冲突而设计的检测器。基于对船舶尺度的统计分析，我们引入金字塔层级偏移策略，将检测头重新配置为P2-P4层级。这一调整确保了对小目标符合奈奎斯特采样准则，同时消除了深层网络的计算冗余。为进一步增强高分辨率输入下的训练稳定性，我们采用了用于线性投影的组归一化卷积块（GN-CBLinear），以缓解微批量设置中的梯度波动问题。在SODA-A、DOTA-v1.5、FAIR1M-v2.0和ShipRSImageNet-V1数据集上的验证表明，LiM-YOLO相比现有最先进模型具有更优的检测精度与效率。代码已发布于https://github.com/egshkim/LiM-YOLO。