# YOLO-SAR: An Enhanced Multi-Scale Ship Detection Method in Low-Light Environments  
[PERSON]  
1 College of Computer Science and Engineering, Guilin University of Technology, Guilin 541006, China; [EMAIL_ADDRESS] (Z.X. ); [EMAIL_ADDRESS] (J.Z.) [PERSON]  
2 College of Physics and Electronic Information Engineering, Guilin University of Technology, Guilin 541006, China [EMAIL_ADDRESS] 21 College of Physics and Electronic Information Engineering, Guilin University of Technology, Guilin 541006, China 21 College of Physics and Electronic Information Engineering, Guilin University of Technology, Guilin 541006, China; 2  
[PERSON]. [7] combined Retime enhancement with receptive-field modules in YOLOv4, achieving effective detection of maritime targets in foggy conditions. Building on Retinex and saliency theory, [PERSON] [8] devised a low-light enhancement framework that fused predicted salient regions with the enhanced image, boosting detection under dim lighting. [PERSON] et al. [9] proposed a lightweight CNN equipped with a CA-Ghost module and hybrid training, which strengthens shallow features and raises accuracy in foggy and nighttime scenes. [PERSON] et al. [10] streamlined YOLOv4 by introducing residual blocks, easing network degradation and markedly improving detection in low-illumination scenes. Finally, [PERSON] [11] used contrastive-learning pre-training to mitigate nighttime feature degradation without adding significant complexity. References [6; 7; 8] highlight target cues through enhanced or fused strategies. However, they have limited capacity for global modeling and fail to rank feature importance, leading to missed detections. The missing detection of the vessel could lead to potential hazards. In response to these negative factors, many effective measures are taken, for instance, incorporating non-local attention mechanisms to enhance long-range dependency modeling, employing dual-path channel-spatial attention for dynamic feature map reweighting, and constructing multi-scale feature pyramid networks to mitigate cross-level semantic conflicts. References [9; 10; 11] optimize network features for low-illumination scenes, yet semantic conflicts across levels remain unsolved. In low-illumination scenes, the misalignment between abstract representations and contour clues prevents existing models from sensibly balancing multi-scale perception. In some complex situations, these core methods do adaptively weight textures and outlines to cope with their specific contexts. Nevertheless, the prior distributions and overall traits of multi-scale cognition there differ significantly from ours, so those schemes lose their edge in our setting. In monocular scenes, extracting high-level clues with deep models can conflict with low-level perceptions such as ship outlines. Clearly, this hampers ship detection in low-illumination scenes. As smart maritime systems and intelligent shipping technologies continue to advance, object detection algorithms are increasingly required to demonstrate high adaptability in complex real-world environments--particularly regarding multi-scale feature representation and fine-grained perception of vessel targets. In nighttime or low-illumination scenarios, vessel imagery often suffers from insufficient luminance, degraded contrast, ambiguous boundaries, and loss of structural details, which collectively undermine the capability of conventional detection algorithms to effectively delineate object contours and morphology. Representative application domains include canal port surveillance, nighttime maritime security patrols, and maritime supervision under adverse weather conditions. The targets encompass heterogeneous vessel types--such as cargo ships, fishing vessels, and passenger ferries--which frequently exhibit visual challenges including large scale variation, partial occlusion, low-texture surfaces, and significant background-boundary fusion.