# YOLO-SAR: An Enhanced Multi-Scale Ship Detection Method in Low-Light Environments
## 4 Results
### Comparative Experiments  
All comparisons in this paper refer to absolute changes measured in percentage points. The experiments were run on a 64-bit Windows 11 system with Python 3.9, PyTorch 2.5.0, CUDA 12.1, and an RTX 3070 GPU with 8 GB of VRAM. The model was trained from zero with an early-stopping strategy: training was halted when validation performance ceased to improve, effectively reducing overfitting. To empirically demonstrate the performance superiority of our proposed YOLO-SAR low-illumination ship-detection framework, we performed a benchmark comparison on the Lowship dataset against other YOLO variants and several representative object-detection models. The experimental results for all key metrics are presented in Table 1.  
Based on the experimental results, the proposed YOLO-SAR framework demonstrates superior performance across multiple evaluation metrics, showcasing enhanced robustness and accuracy in low-light object detection. Traditional detection algorithms, such as Faster R-CNN and SSD, show limited effectiveness under low-illumination conditions due to their dependence on shallow convolutional backbones, which are insufficient in capturing weak textures and low-contrast features. These methods are highly sensitive to illumination degradation and edge information, often resulting in false negatives and false positives under challenging conditions such as image blur or poor lighting, rendering them inadequate for ship detection in low-light scenarios. Although YOLOv3-tiny achieves extremely high inference speed, its lightweight architecture compromises its feature representation capability and lacks an effective multi-scale feature perception mechanism, leading to degraded detection performance on small or low-texture targets. Specifically, compared to YOLOv3-tiny, YOLO-SAR achieves improvements of 11.1% in precision, 10.2% in recall, and 9.3% in [EMAIL_ADDRESS], highlighting its ability to maintain high detection performance under complex low-light conditions. Although recent mainstream YOLO series models--such as YOLOv5, YOLOv8n, YOLOv9t, and YOLOv10n--achieve a good trade-off between accuracy and speed under general lighting conditions, their network architectures are not specifically optimized for the characteristics of low-light imagery. In particular, they exhibit clear limitations in shallow-level edge representation and cross-scale semantic alignment. Most of these models adopt static feature aggregation schemes, which lack the ability to dynamically adjust multi-scale feature weights based on image content. As a result, they struggle to resolve scale-level semantic inconsistencies prevalent in low-light environments. Experimental results show that under identical testing conditions, YOLO-SAR outperforms the aforementioned models by 3.2%, 2.6%, 4.7%, and 3.5% in terms of [EMAIL_ADDRESS], respectively, demonstrating its superior overall performance in low-light object detection scenarios. Despite the introduction of multiple modules that introduce a certain computational overhead, YOLO-SAR maintains an inference speed of 51.7 FPS. This indicates that the overall real-time performance remains within acceptable limits and is sufficient to meet the requirements of most practical applications. To further evaluate the generalization capability and practical applicability of the proposed YOLO-SAR framework under varying low-light conditions, a real-world ship image dataset was collected at dusk from the Yujiang River Basin in Guilin, Guangxi. This dataset consists of 8529 images captured in representative inland open-water scenarios. The ship targets exhibit varied viewpoints, object scales, and complex backgrounds, and include representative vessel classes such as cargo ships and ore carriers. The images were acquired under dusk lighting conditions, closely simulating real nighttime application environments. The data acquisition setup and parameter configurations are consistent with those used in the Lowship dataset. Example images are provided in Figure 10.