# YOLO-SAR: An Enhanced Multi-Scale Ship Detection Method in Low-Light Environments
## 5 Conclusions and Future Directions  
To address the challenges of ship detection in low-light environments--such as degraded target features and limited image contrast, which significantly hinder the deployment of existing detection algorithms--this paper proposes an enhanced detection framework for low-light ship imagery, named YOLO-SAR. Extensive empirical evaluations on both a custom-built dataset and a real-world dataset demonstrate its effectiveness and practical potential under challenging illumination conditions. To address the limited availability of low-light ship imagery, this paper presents a preprocessing approach based on image degradation modeling and introduces the Lowship dataset. The proposed method simulates realistic nighttime conditions by applying exposure suppression, noise injection, gamma correction, and tone adjustment. Its effectiveness is quantitatively validated through comparative experiments using grayscale intensity and HSV color space metrics. In terms of architecture, the YOLO-SAR framework integrates the SCINet module to enhance perceptual image quality. It incorporates receptive-field attention convolution (RFAConv) to strengthen global contextual representation and adopts an adaptive spatial feature fusion (ASFF) mechanism in the detection head to address semantic inconsistencies across feature hierarchies. Comparative experiments on the Lowship dataset demonstrate that YOLO-SAR achieves significant improvements in detection performance. It surpasses YOLOv5, YOLOv8n, YOLOv9t, and YOLOv10n by 3.2%, 2.6%, 4.7%, and 3.5%, respectively, in terms of [EMAIL_ADDRESS]. Ablation studies further validate the individual contributions of each module and highlight the cumulative benefits of the overall architecture in improving detection accuracy. To assess the model's real-world applicability and cross-domain generalization, a low-light ship image dataset was collected from the Yujiang River Basin in Guilin, Guangxi. Experimental results show that YOLO-SAR continues to deliver state-of-the-art performance on this real-world dataset, demonstrating strong generalization capabilities and promising potential for practical deployment. Although YOLO-SAR demonstrates notable performance gains in low-light object detection, it still presents several critical limitations in real-world applications that require further improvement. First, under conditions of extreme darkness, complex occlusion, or severe noise, the model tends to generate both false negatives and false positives, compromising detection robustness. Second, the current model incurs high computational costs, including substantial parameters and inference latency, which restrict its deployment on resource-constrained embedded or edge platforms. Additionally, while the proposed low-light preprocessing method enables the generation of synthetic datasets and has undergone initial validation using grayscale and HSV domain comparisons, it lacks a systematic analysis of parameter configurations and a comprehensive quantitative evaluation of its effectiveness. To address these limitations, future work will focus on the following directions: (i) exploring structural pruning, lightweight attention mechanisms, and knowledge distillation to improve inference efficiency while maintaining accuracy; (ii) designing a multi-scale fusion architecture with scalability and pruning flexibility to enhance real-time performance and deployment adaptability on embedded platforms; (iii) systematically evaluating the impact of image degradation parameters on both image quality and detection accuracy, and establishing a quantitative correlation framework between preprocessing strategies and detection outcomes, thereby enabling automated preprocessing selection and scenario-specific optimization. **Author Contributions:** Conceptualization, Z.X. and M.W. ; software, Z.X. ; experiment analysis, Z.X. and J.Z. ; writing--review and editing, Z.X., M.W., and R.K.; funding acquisition, M.W. and R.K.; investigation, Z.X. ; visualization, Z.X. ; supervision, Z.X. and M.W.