# YOLO-SAR: An Enhanced Multi-Scale Ship Detection Method in Low-Light Environments
## 4 Results  
### Dataset Preprocessing  
Due to the limited availability of low-light ship datasets, this study incorporates images independently captured in the Yujiang River Basin, Guilin, Guangxi. The imaging device used was the Hikvision DS-2 DC7423 IW-AE surveillance camera. All images were recorded in 1920 \(\times\) 1080 resolution and H.264 format, with a fixed shutter speed of 1/1000 s and ISO set to auto. To enrich data diversity, additional samples were selected from the publicly available SeaShips dataset. The collected and selected images primarily depict open inland waterways, featuring vessels with varying viewing angles, scales, and background complexities. The vessels are categorized into five types: bulk carriers, container ships, fishing vessels, ore carriers, and passenger ships. As these images were all captured under normal lighting conditions, with several examples presented in Figure 6, a low-light simulation method was proposed to transform them into corresponding low-illumination scenarios. The construction pipeline of the resulting low-light dataset is elaborated in the following sections. To simulate the underexposure characteristics of sensors in low-illumination scenarios, each pixel is multiplied by a randomly sampled exposure coefficient exposure \(\in[0.1,\,0.4]\), allowing the image to retain visible details in low-light environments. Gaussian sensor noise is then added to the exposure-adjusted image to the additional quality degradation caused by hardware limits. We employ a Gaussian-noise model with intensity sensor \(=0.03\), meaning that noise accounts for 3% of the 0-255 gray-level range. Following these modifications, gamma correction is applied to refine the luminance distribution curve. This correction comprehends the nonlinear response of sensors in low-illumination environments, with the gamma value \(\gamma\) being randomly drawn from [1.0, 1.5]. The exact computation is given in Equation (6). \[I_{out}=\left(\frac{I_{in}}{255}\right)^{\gamma}\times 255 \tag{6}\]  
\(I_{in}\) and \(I_{out}\) denote the pixel values before and after correction, respectively. The image contrast is then slightly adjusted to emulate the compressed dynamic range typical of low-illumination scenes; the contrast coefficient must be kept within a reasonable range to avoid unnaturally strong or weak contrast [36]. In the end, to enhance nighttime realism, a mild tone adjustment is applied, particularly by amplifying the blue channel to replicate the cool tint commonly observed in nocturnal images [37]. This effect is achieved by decreasing the intensity of the red channel while increasing that of the blue channel. After the multi-stage processing described above, images captured under normal lighting now exhibit the hallmarks of low-illumination scenes--reduced brightness, heightened noise levels, weaker contrast, and a slight color cast. These operations faithfully mimic a sensor's response to external light in low-illumination scenes, bringing the images closer to real-world capture conditions. The same procedure is used to create a low-illumination ship dataset called Lowship, which contains 12,606 images belonging to a single class. The dataset is randomly divided by a ratio of 8:2, resulting in 10,084 training images and 2522 validation images. Several samples from the dataset are presented in Figure 7.  
To evaluate the effectiveness of the proposed dataset-preprocessing strategy, comparative experiments are conducted in grayscale and HSV color spaces on both the original dataset and the low-illumination ship dataset generated through this method. In the experiments, grayscale values and the three HSV channels--hue (H), saturation (S), and value (V)--are extracted from ship images in both datasets and represented as histograms. Figure 6: Selected sample images (samples of various categories of vessels under normal daylight conditions in open inland waterways).