## 5 Discussion  
Mobile vehicle video data were greatly influenced by the environment and had variability. These videos were divided into many frames to enable frame-by-frame playback, creating a coherent dynamic effect. Finding usable images and adjusting their quality to increase annotation were necessary for obtaining an effective training model. CCTV data focused their analysis on a fixed area where the environmental impact was minimal. The fixed scene only required software optimization for the impact of traffic lights on fixed positions. However, the changing scenes in this study required a consideration of the clarity of traffic light recognition, as well as the changes in surrounding lighting and objects for appropriate software optimization. The contribution performed mobility data for model training had a wider range of applications not limited to a fixed area. This study analyzed events that occurred after collecting mobility video data. Therefore, this study is a post-analysis. Future research will collect CCTV image data to compare and analyze together.  
Due to the poor quality of the collected fleet images, this study provided more flexible image enhancement adjustments using the fuzzy method. This study also considered the option for overall brightness after fuzzy enhancement. Thus, this paper proposed improving image pre-processing using Equation (2). In the future, as more large fleet images are collected, the testing and adjustment of fuzzy enhancement will continue to solve the multiple resolutions in fleet management.  
In practice, the different quality of cameras installed in many large vehicles varies, so properly utilizing existing data for annotation is important. With limited data, finding more traffic light data was challenging. Some vehicles frequently traveled through fixed routes where the traffic lights were the same, but the lighting conditions varied daily.  
Figure 13: Confusion matrix of YOLOv9 results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline YOLOv9 with histogram equalization & 0.7430 & 0.8736 & 0.8325 & 0.8525 \\ enhancement & & & & \\ YOLOv9 & 0.7500 & 0.8770 & 0.8381 & 0.8571 \\ YOLOv9 with fuzzy enhancement & 0.8248 & 0.9169 & 0.8914 & 0.9040 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Testing evaluation for detection.  
Choosing suitable traffic lights manually for annotation took time, thereby improving the original images directly was more effective. YOLOv9 with fuzzy enhancement has demonstrated improved performance in pre-processing fleet images. YOLOv9 exhibits substantial advancements compared to older versions. In further studies, older versions of YOLO can also be used for comparison, as different versions have proven useful for handling various image cases in the literature. The older versions of YOLO performed better in optimizing specific image-processing tasks and offered better compatibility with existing systems.  
A limitation of the study is that it did not consider nighttime conditions, as well as adaptive parameters and brightness adjustments. Moreover, based on the equipment limitation, the car kits using 4G and its bandwidth cannot transmit full HD video back. In the future, the equipment will be upgraded to 5G, which has high-speed and low-latency characteristics and is expected to make the video return full HD or higher quality. However, this would require additional expenditure to purchase equipment. In practice, not every large vehicle fleet had the budget to install the same equipment. Therefore, using pre-processing is still the best solution. Further work will collect more video data and add more adjusted images to enhance the training model.