## 3 Methodology  
This paper emphasized the importance of pre-processing and proposed an improved method to overcome moving image enhancements with multiple resolutions. Appropriate pre-processing can enhance the quality of original images by reducing unnecessary information and noise to solve poor image problems. Resolution refers to the detail an image can display, and it is measured in pixels, which specify the dimensions of the image in terms of width and height. Image enhancement can improve the clarity of images, making them easier to identify and analyze reliably. Traditionally, the basic method involved adjusting the RGB colors. When the image quality was acceptable, directly adjusting the RGB colors effectively enhanced the visual appearance of the image. By adjusting the red, green, and blue colors, contrast, brightness, and color balance can be improved without the need for complex processing. The popular pre-processing method was histogram equalization [5; 33]. Histogram equalization was a widely used pre-processing method for images, focused on improving their quality. Histogram equalization was also used to adjust the distribution of pixel intensities to enhance poor images, making details more visible. When image quality was poor, a flexible adjustment method was chosen based on specific situations to achieve optimal image-enhancement results. The fleet images were greatly affected by changes in road lighting, and we considered proposing a more flexible approach to handle different resolutions. Fuzzy logic introduced fuzzy sets into logical operations so that inferences and judgments were no longer only true or false but could include intermediate fuzzy values. Fuzzy inference is a reasoning process based on fuzzy logic, usually using a fuzzy rule base and fuzzy inference engine. Therefore, a flexible fuzzy enhancement method was adopted to effectively enhance poor image quality. Since color comprised various shades for distinction, it was less likely to be misjudged [58]. [PERSON] and [PERSON] [59] proposed a fast and efficient fuzzy, which was transferred to other color spaces that handled low contrast and low bright color images. In previous studies, there have been discussions on two methods for adjusting image brightness. The first involved converting RGB to grayscale using multiplication followed by addition, which results in faster computational performance. The second method adjusted brightness and contrast, utilizing the linear function. When there was a lot of noise in the images of traffic lights, it may still interfere with the judgment results, as only black and white colors were used for judgment [60; 61; 62]. Based on the above conception, color conversion allowed for more parameter adjustments during image enhancement. In practice, fleet managers monitored multiple color video feeds, as grayscale images that display shades of gray, ranging from black to white, without any color are not suitable. The key solution lies in pre-processing. The proposed fuzzy enhancement method increased image contrast and suppressed noise using OpenCV [63]. In the past, the biggest challenge encountered in research was that the truck videos received from sensors were under insufficient light. When using the OpenCV packages for testing, erroneous results still occurred. Using the concept of weighting, fuzzy theory provided appropriate weight value fuzzification variables, so that through weight value adjustment, an improved and corrected value was obtained, which can subsequently be used to improve the original identification and poor training results and enhance video-feature judgment. The study discussed two types of images, including (1) images of the front view, and (2) images containing traffic lights. Therefore, all images contain traffic lights. Figure 1 is the flowchart, using three methods to enhance images. There are six steps as follows. 1. This study collected the front camera video data from large fleets. 2. The video is split into individual images, and all images contain traffic lights. 3.