LiM-YOLO: Less is More with Pyramid Level Shift and Normalized Auxiliary Branch for Ship Detection in Optical Remote Sensing Imagery

• URL: http://arxiv.org/abs/2512.09700v1
• Date: Wed, 10 Dec 2025 14:48:58 GMT
• Title: LiM-YOLO: Less is More with Pyramid Level Shift and Normalized Auxiliary Branch for Ship Detection in Optical Remote Sensing Imagery
• Authors: Seon-Hoon Kim, Hyeji Sim, Youeyun Jung, Ok-Chul Jung, Yerin Kim,
• Abstract summary: LiM-YOLO is a specialized detector designed to resolve domain-specific conflicts.<n>Based on a statistical analysis of ship scales, we introduce a Pyramid Level Shift Strategy that mitigates the detection head to P2-P4.<n>LiM-YOLO demonstrates superior detection accuracy and efficiency compared to state-of-the-art models.
• Score: 3.2425852744760184
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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