# SE-Lightweight YOLO: Higher Accuracy in YOLO Detection for Vehicle Inspection
## 1 Introduction  
An intelligent transportation system (ITS) represents a crucial form of technology that efficiently addresses common traffic issues and enhances overall traffic safety [1]. It can be divided into four aspects: reducing travel time, ensuring traffic safety, relieving traffic congestion, and reducing traffic pollution. The ITS has the undeniable potential to ensure safer and smoother traffic on the road [2]. As the global population continues to grow and urbanization accelerates, problems such as road congestion and increased pollution emissions are becoming more serious, and the need for intelligent transportation is becoming more and more urgent. According to market monitoring data, the global intelligent transport market is expected to exceed USD 250 billion by 2025 [3]. The intelligent transportation industry concentration is relatively low, and the financial strength and scale of enterprises in the same industry are generally small. The market competition is also mainly concentrated in a certain regional scope and a small number of enterprises. The emerging Internet and IT giants having cross-border access to the intelligent transportation industry will greatly impact the competition and developmentof the industry; moreover, it is generally expected that the market concentration of the intelligent transportation industry will further increase in the future. Ultimately, traffic congestion results from disruptions to the typical flow of vehicles, particularly those moving in a straight direction. The pivotal factor contributing to these disruptions is the alteration of traffic light signals [4], significantly hindering most vehicles' smooth operation. Traffic congestion is generated precisely because the traffic light signals are not set up reasonably. Traffic signals are a silent commander on the highway, playing an irreplaceable role in traffic safety to improve driving efficiency and ensure the lack of traffic congestion; however, it is often unsatisfactory. Currently, most cities use \"timed\" traffic lights with a fixed red light-green light time interval [5]. Due to the \"timing\" of traffic lights, the duration of the traffic light cannot be based on changes in road conditions or real-time adjustments, as these conditions are difficult to satisfy. Using timed traffic lights will often lead to vehicle congestion at intersections with high or changing traffic flows. Given the complexity and variability of traffic conditions, only by adjusting traffic lights in real-time according to the specific situation can we take into account the safety of driving and the efficiency of the use of road resources [6]. With the rapid development of the Internet of Things and artificial intelligence, it has become possible to use information technology to adjust traffic signals according to real-time road conditions. Most traffic signals are equipped with high-definition cameras that can dynamically analyze the situation of vehicles at intersections based on the real-time images and videos they capture, and automatically adjust traffic lights according to the real-time needs of different road sections. In this case, traffic congestion can be reduced to a minimum while ensuring traffic safety. However, the recognition of vehicles and other means of transportation at intersections cannot better identify all types of vehicles and pedestrians nowadays. In addition, the recognition accuracy needs to be improved; for this reason, this paper proposes the SE-Lightweight YOLO model, which is designed to address the current state-of-the-art research and help to improve the situation of lower classification accuracy and fewer classification categories. At the same time, improving the accuracy of the camera's recognition of vehicles is of great significance to further ITS development and improve traffic smoothness. This paper proposes an effective method for vehicle recognition to address the problems based on the above analysis.