## 1 Introduction  
In recent years, the increasing car ownership in China has led to traffic congestion, long commute times, traffic accidents, and environmental problems. To address these issues, authorities have resorted to infrastructure construction methods such as building viaducts and widening roads. However, these solutions are resource-intensive and do not tackle the root cause of the problem. Therefore, several developing countries are focusing on developing technologies related to intelligent transportation systems (ITS) to improve traffic management effectiveness [1]. Vehicle detection is an essential auxiliary system of ITS. With the evolution of computer vision, vehicle detection technology has become a topic of significant interest among scholars. Vehicle detection, being the primary task of intelligent transportation systems, plays a crucial role in determining the efficacy of the entire intelligent system. Motion-based vehicle detection methods can be categorized into three types: optical flow [2], frame difference [3], and background difference [4]. The optical flow method is based on optical flow fields, which are first extracted from the image and combined with image feature information for target detection. However, this method is computationally intensive and cannot achieve real-time detection. The frame difference method detects motion regions by subtracting two adjacent frames in a video sequence, which generates a large number of voids when traffic video speeds are high. The background difference method involves building a background model and comparing it with the video sequence frame by frame. If the video image frame is the same as the exact position of the built background model, it is identified as the background and updated to the background model. However, this method is less robust, more sensitive to illumination and noise, and more time-consuming to reconstruct than the background model. [PERSON]'s proposed background modeling algorithm employs inter-frame difference and the Gaussian mixture model while also utilizing the Harris corner detection method to accurately detect moving objects. Although the algorithm exhibits impressive detection accuracy and real-time performance, its practical application still requires further development [5]. [PERSON] introduced a method for enhancing vehicle camera monitoring systems which relies on fast background modeling and adaptive moving target detection. The core idea of this method is to constantly update the background model to enable for the efficient detection of moving targets within real-time video streams. Although the approach showcases promising results, its detection outputs might not suffice to support follow-up research. Further research and refinement are necessary to enhance its detection capabilities and maximize its potential in vehicle monitoring applications [6]. With the continuous advancement of machine learning and GPU parallel processing capability, deep learning-based target detection has become a popular research topic. It can be classified into region candidate-based target detection and regression-based target detection. Region candidate-based target detection, also known as the Two-Stage target detection algorithm, generates candidate regions and classifies them based on regions, with the R-CNN series [7; 8; 9] being a classical algorithm in this category. Regression-based target detection algorithms, also known as One-Stage target detection algorithms, can simultaneously identify targets and determine their location. Examples of representative algorithms in this category include SSD [10] and YOLO series [11; 12; 13; 14; 15]. The original YOLOv5 model suffers from generating redundant feature information during feature extraction in vehicle detection, leading to a large model size and slow inference.