## 3 Methodology
This study conducted pre-processing tasks for image adjustment, including (i) direct RGB adjustment, (ii) histogram equalization with RGB and CIELAB conversion, and (iii) proposed fuzzy enhancement with RGB and CIELAB conversion, which belonged to the pre-processing task. The proposed fuzzy enhancement with brightness adjustment was optional. The most direct methods adjust RGB colors, which can produce unsuitable images. CIELAB is a flexible color model method used for adjusting color values. As CIELAB space was independent of light sources and devices, it processed at speeds comparable to RGB. There were three components for CIELAB color space [64]. CIELAB can be denoted as Lab, and L denotes luminance, a is the range from red to green, and b is the range from blue to yellow. Among all color spaces, CIELAB mode can express the most abundant colors, exceeding RGB mode. CIELAB color space was designed to closely mimic human vision, focusing on providing a consistent perceptual experience. Based on the images used in [64; 65], there were no complex situations involving multiple light effects and different resolutions compared to our large fleets study. Thus, this study conducted the appropriate designs and adjustments in Section 4. The purpose of the CIELAB color space was to mimic human vision, ensuring that the Euclidean distance between two colors corresponds to  
Figure 1: Flowchart for image enhancement [40]. their perceived difference. Using OpenCV, the cvtColor function can be used to convert between CIELAB and RGB images. For the proposed fuzzy enhancement to solve multiple resolutions, this study provided detailed descriptions. First, we considered using the Gaussian function since the Gaussian function is continuous and smooth, which provides better tolerance to image noise. The fuzzy process used the Gaussian function for image enhancement. We converted the input image from the RGB color space to CIELAB, which represents the most complete color model describing all the colors visible to the human eye and visual perception. The previous study considered that fleet images directly used RGB processing, which limited the ability to adjust color corrections. When images are collected from a camera lens with poor image quality, using CIELAB can provide a more effective method for corrections. Second, in the process of fuzzification, to avoid adjusting excessively dark near 0 or over bright near 255, this study set pixel intensity as the midpoint at 128. Through designed fuzzy inference rules, the output fuzzy set was computed. In the final defuzzification stage, the output fuzzy set was processed using the learning method and normalized. After these color-correction procedures, the result was converted back to RGB. According to [66], the contrast and brightness adjustments  
\[g(x)=\alpha f(x)+\beta \tag{1}\]  
where \(\alpha\) denotes control contrast that is the gain and \(\beta\) denotes bias parameters that are the brightness, \(f(x)\) is the source image pixels and \(g(x)\) is the output image pixels. The concept of Equation (1) suggests that, in this study, modified fuzzy enhancement is denoted as \(\alpha f(x)\) and brightness adjustment as \(\beta\). This study considered \(\alpha\) as equaling 1. Since the fuzzy enhancement has already been completed using the function \(f(x)\), the adjustment of the fuzzy enhancement weights was not considered. \(\beta\) (Brightness) is a constant. Therefore, the proposed method can be simply rewritten as  
\[Fuzzy enhancement+Brightness Adjustment \tag{2}\]  
After fuzzy enhancement, if the fleet manager considered that the overall image brightness was increased, brightness adjustment was considered a tuning mechanism. Brightness adjustment was optional and was potentially not necessary, depending on the image situation. 1. To evaluate the front images and traffic lights, this study used mean squared error (MSE) and peak signal-to-noise ratio (PSNR) evaluation metrics to compare methods of histogram equalization and fuzzy. 2.