# YOLO-SAR: An Enhanced Multi-Scale Ship Detection Method in Low-Light Environments
## 2 Related Work  
### Related Research  
Research on object detection is usually categorized into traditional pipelines and deep-learning-based algorithms [12]. Early approaches heavily relied on hand-crafted features: the Viola-Jones detector [13] employed a sliding window technique to scan the image for potential targets, while the Deformable Part Model [14] combined enhanced Histogram of Oriented Gradient features [15] with a Support Vector Machine classifier [16] and bounding-box regression [17]. These conventional pipelines require substantial computational resources and struggle to meet today's demands for low-illumination scenes and real-time operation. Deep learning detectors can be broadly classified into two categories: two-stage and one-stage methods. Two-stage methods first generate region proposals and then perform classification and regression with a convolutional network. Classic examples include R-CNN [18] and Faster R-CNN [19]. One-stage methods make end-to-end predictions on the whole image, typified by the Single Shot MultiBox Detector [20] and You Only Look Once [21]. Two-stage approaches often yield higher accuracy but run slowly, whereas one-stage algorithms such as YOLO drastically improve real-time detection [22]. In recent years, a significant number of studies have explored the YOLO model families for the purpose of ship detection. [PERSON] et al. [23] have utilized YOLOv7 as their baseline and developed an end-to-end feature fusion and enhancement scheme that integrates channel attention with a spatial channel pooling block, thereby strengthening the synergy between multi-scale clue comprehensions and network structures. [PERSON] et al. [24] have refined YOLOv5 by incorporating a task-specific module designed to suppress conflicting information, which enhances the detection of fine ship details. [PERSON] et al. [25] have introduced an adaptive multi-scale variant of YOLO that is combined with a multi-granularity adaptive feature enhancement module. Their approach also employs grouped weighting and various adaptive mechanisms to enrich detail extraction and fuse multi-scale features with contextual information, thereby increasing applicability in dynamic maritime environments. [PERSON] et al. [26] propose YOLO-Ships, a lightweight yet effective method that upgrades the Ghost module to amplify feature extraction and adopts a ship-feature enhancement block, thus balancing compactness with detection accuracy. As for methods employed in low-light conditions, many existing YOLO models demonstrate notable capabilities in object detection (such as YOLOv3, YOLOv4, YOLOv5, and YOLOv7). However, they also exhibit several limitations when confronted with the challenges posed by low-illumination environments. First of all, conventional YOLO models often struggle with feature extraction in low-light conditions, particularly in recognizing fine details in real-world situations. These negative factors often lead to increased image noise and diminished contrast, resulting in the loss of critical features that adversely affect detection accuracy. In addition, the performance of current YOLO algorithms can be unstable in complex low-light scenarios. It results in the loss of critical features that adversely affect detection accuracy. Moreover, many models lack the ability to adaptively adjust to changes in lighting conditions. This underscores the need for optimization to enable effective responses to diverse illumination scenarios. Last but not least, the introduction of complex computations to enhance detection accuracy can detrimentally affect their real-time performance, while these models are celebrated for their rapid inference speed. Therefore, it is necessary to take related and necessary measures. Collectively, these works keep YOLO's speed while boosting ship-detection capability through attention, custom modules, and multi-scale fusion, yet deficiencies remain under low-illumination scenes. Accordingly, further architectural upgrades are warranted for such conditions.