## 4 Results  
There are three main pre-processing tasks for comparisons, including direct RGB adjustment, histogram equalization with RGB and CIELAB conversion, and proposed fuzzy enhancement with RGB and CIELAB conversion. This paper collected video data from cameras installed on moving large vehicles (i.e., trunks) and used Python and the OpenCV library to automate the extraction of a specific number of frames per second from the video data that included multiple image resolutions. We collected daytime video data of large vehicles between 2023 and 2024. This included 25 trucks and a total of 417 videos as detailed in Table 2. Each video was approximately 10 minutes long. The majority of collected large vehicle videos represented driving on normal roads. We labeled traffic light images from the limited video data. This study used 1837 images, with 1480 for training data (65.34%), 357 for validation data (15.76%), and 428 for testing data (18.90%). Generally, common resolutions include Ultra High Definition (Ultra HD), Full High Definition (Full HD), High Definition (HD), Standard Definition (SD), and Low Resolution. According to the collected video data from large vehicle fleets, the commonly used cameras had resolutions below Full HD. This was because most fleet managers considered costs. The managers chose color videos, and vehicles had different camera installations, resulting in varying resolutions. This study used the Python Imaging Library (PIL) package to \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & \multicolumn{3}{c}{**Data**} \\ \cline{3-5}  & **Truck** & **Video** & **Training** & **Validation** & **Testing** \\ \hline \multirow{2}{*}{Quantity} & \multirow{2}{*}{25} & \multirow{2}{*}{417} & 1480 (65.34\%) & 357 (15.76\%) & 428 (18.90\%) \\ \cline{3-5}  & & & & Total 2265 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Data information. determine the resolution category based on width and height in Table 3. The total training data were 1480, with 1447 belonging to HD, 16 belonging to SD, and 17 belonging to low resolution in Figure 2. There were 16 SD and 17 low resolution that had poor image quality compared to HD. In actual deliveries, the drivers usually chose to take the route with green lights and the smoothest flow. Due to time constraints for every deployment of tasks, drivers often tried to avoid red lights to prevent delays. Fleet managers were also concerned about the number of times drivers ran red lights. This sparked an interest in traffic lights, and the objects were also considered from full front images to small traffic lights. Based on data information in Table 2, the total image data was 1837, with 1480 for training and 357 for validation. In this study, all 1837 collected images contain traffic lights, with classes including green, red, yellow, and green left turn lights. The number of annotations included 1073 for green, 579 for red, 157 for yellow, and 28 for green left turn in Figure 3. According to the traffic light image annotations, the number of annotations was greater than red lights, yellow, and green left turns. According to the number of traffic lights, it is necessary to add annotations to these four lights. \begin{table}
\begin{tabular}{c} \hline \hline
**Processing Image Files Using Python Imaging Library (PIL) Version 10.4.0** \\ \hline if width \(\geq\) 3840 and height \(\geq\) 2160: \\ return “Ultra HD” \\ elif width \(\geq\) 1920 and height \(\geq\) 1080: \\ return “Full HD” \\ elif width \(\geq\) 1280 and height \(\geq\) 720: \\ return “HD” \\ elif width \(\geq\) 640 and height \(\geq\) 480: \\ return “SD” \\ else: \\ return “Low Resolution” \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Determine the resolution category based on width and height. Figure 2: Image counts by resolution category. This study selected the most challenging poor-quality images from among 33 with resolutions smaller than HD as an example. This study required addressing the pre-processing through image enhancement.