## 1 Introduction  
In recent years, the Internet of Vehicles (IoV), combined with machine-learning technology, has been booming, creating diverse applications in intelligent transportation. Machine-learning methods can be applied to image-processing tasks, such as image classification, annotation, pre-processing, and object detection, thereby enhancing accuracy and efficiency. Machine learning is crucial to the development of artificial intelligence (AI) technologies, and deep learning is a subset of machine learning. AI can enhance the efficiency of traffic management. Even geospatial artificial intelligence (GeoAI) combines AI and geographic techniques to explore and apply sensor networks in cities to reduce traffic congestion [1; 2; 3; 4; 5]. Among the key technologies, sensors are the primary tools for gathering video data to monitor vehicle conditions. Sensor systems can play a crucial role in providing video data for intelligent transportation management [6; 7]. Currently, many fleet management systems involve managers dispatching personnel to review and monitor events as they occur. However, many of the images have low resolutions with poor-quality images, resulting in managers not conducting further analysis [8; 9; 10]. Most image-processing research mainly focused on observing road conditions and pedestrian traffic, with few studies addressing cases of front cameras installed in large vehicle fleets. Therefore, this paper proposes an improved pre-processing method in large fleet operations. In the current intelligent transportation applications, commonly used sensors include: (1) closed-circuit television (CCTV) for detecting traffic violations and vehicle flow, and (2) vehicle front cameras for monitoring external traffic conditions. Many previous studiesfocused on transportation research using CCTV. CCTV is widely applied in many different intersections and roads to monitor traffic conditions and is also used to monitor traffic violations, such as running red lights and speeding [2; 6; 11; 12; 13; 14; 15; 16]. However, the disadvantages of CCTV include a limited field of view, as cameras typically only cover a fixed range. If a vehicle accident occurs outside of this range, it cannot be captured. Additionally, when lighting is poor or at a distance, the camera's image quality cannot be sufficient to identify front objectives. Moreover, the previous study had discussed using CCTV to determine red light running violations [16], and this has evolved into a popular topic in technology-driven law enforcement. Although traffic lights look small, they can still be used in conjunction with stop lines to determine if a vehicle has run a red light. In the context of vehicle CCTV, mobile lenses are suitable for fleet management due to their convenience. In practice, vehicles do not install cameras that monitor the ground, making it difficult to determine if a vehicle has crossed a stop line. Thus, this paper focused on front image pre-processing and analyzed the impact of the pre-processing on the traffic lights. This paper collected the mobility videos installed by a front camera in large vehicles; the front camera is more flexible and can reflect real traffic conditions. The managers usually choose cheaper cameras. Since the camera equipment collected in practice was varied in resolution, the video images will directly affect the training results. Humans may not always be able to see clearly, let alone machines making accurate judgments. In past research [5], a vehicle with a larger size and obvious features, such as its body shape, is easier to identify even when the image has low resolution. The size of traffic lights is smaller than vehicles. Under poor image conditions, traffic lights are small and difficult to recognize. Therefore, this paper proposed image enhancement using fuzzy to improve the pre-processing problem to handle multiple resolutions. The comparison of CCTV and mobility cameras is shown in Table 1.  
The traffic light is small on a video screen. Increasing data for training can be one option.