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

Abstract

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. In this work, we propose LiM-YOLO (Less is More YOLO), a streamlined detector designed to address these domain-specific structural conflicts. Through a statistical analysis of ship scale distributions across four major benchmarks, we introduce a Pyramid Level Shift Strategy that reconfigures the detection head from the conventional P3-P5 to P2-P4. This shift ensures compliance with the Nyquist sampling condition for small targets while eliminating the computational redundancy inherent in the deep P5 layers. To further stabilize training on high-resolution satellite inputs, we incorporate a Group Normalized Convolutional Block for Linear Projection (GN-CBLinear), which replaces batch-dependent normalization with Group Normalization to overcome gradient instability in memory-constrained micro-batch regimes. Validated on SODA-A, DOTA-v1.5, FAIR1M-v2.0, and ShipRSImageNet-V1, LiM-YOLO achieves state-of-the-art detection accuracy with significantly fewer parameters than existing methods, validating that a well-targeted pyramid level shift can achieve a "Less is More" balance between accuracy and efficiency. The code is available at https://github.com/egshkim/LiM-YOLO.