# Research on Vehicle Target Detection Method Based on Improved YOLOv8
## 1 Introduction  
With the rapid development of the economy, transportation has become an important industry that supports the country's economic growth. The ensuing traffic congestion and safety hazards have become increasingly prominent, seriously affecting people's travel efficiency and quality of life. With respect to the driver, improper action usually leads to a higher risk of traffic accidents [1]. Numerous developing countries are prioritizing intelligent transportation systems to improve the efficiency of traffic management [2]. Vehicle target detection can significantly improve the convenience and comfort of public travel and improve overall quality of life, provide support for intelligent transportation systems, and contribute to the intelligent and sustainable development of urban transportation. Traditional non-intelligent algorithms typically have the advantages of simplicity and low computational cost [3]. There are three main vehicle inspection methods. The background update method [4] relies on weighted averaging to refresh the background, but this can adversely affect the completeness of target extraction and reduce detection accuracy [5]. The frame difference technique [6] detects targets by calculating the difference between consecutive frames; however, it is highly sensitive to factors such as vehicle speed and time gap between frames [7]. The optical flow method [8], on the other hand, is atechnique for estimating the density at the pixel level [9]. The detection process begins with the generation of numerous candidate frames using sliding windows of varying sizes. The features are extracted from these frames, which are then used in conjunction with a classifier to detect and precisely determine the location of each target. However, the large computational load poses significant challenges for real-time target detection. The vehicle detection features communicated include Scale Invariant Feature Transform (SIFT), Histogram of Oriented Gradient (HOG), Haar-like Features (Haar), etc. [10; 11; 12; 13], while classifier devices include Support Vector Machine (SVM), Adaptive Boosting (Adaboost), etc. [14; 15]. Common vehicle detection models are Haar + Adaboost, HOG + SVM, and the Deformable Part Model (DPM) [16]. Traditional feature selection methods rely on a priori knowledge, but in real application scenarios, multiple objective interferences such as light changes and deformation exist. Detection algorithms face challenges when applied in real-world situations, often lacking the precision and robustness needed for practical implementation [17; 18; 19]. With the advent of the Convolutional Neural Network (CNN), researchers have found that the feature extraction process in CNN mirrors the human visual system, progressing from edge detection to local features and ultimately to the whole image [20; 21]. Since the beginning of the 21 st century, the emergence of deep learning has revolutionized computer vision, shifting it from dependence on manually crafted features to attaining higher levels of accuracy and intelligence [22; 23]. Vehicle detection often encounters challenges such as varying object scales, dense traffic, and real-time processing requirements. The Region-CNN (R-CNN) [24] framework introduced a two-stage approach using a selective search for proposal generation, but it is slow and computationally expensive. Fast R-CNN [25] improved efficiency by extracting features from the entire image, but still relied on external proposal methods. The Faster R-CNN [26] further introduced a Region Proposal Network, enabling faster and more accurate detection. Despite the fact that these models excel in complex scenes with occlusion, their significant computational demands restrict their applicability to real-time vehicle detection systems. To cater to the real-time demands of vehicle detection in intricate traffic environments, single-stage object detection algorithms have gained popularity.