# Research on Vehicle Target Detection Method Based on Improved YOLOv8
## 1 Introduction
These approaches eliminate the region proposal step utilized in two-stage detectors, enabling faster inference with relatively lower computational overhead. The Single Shot Multibox Detector (SSD) [27] stands out as one of the initial single-stage models, enhancing detection speed through the incorporation of multiscale feature maps. However, SSD typically struggles to identify small objects, which is a common occurrence in distant or partially obstructed vehicle instances. In contrast, the You Only Look Once (YOLO) series [28] reimagines detection as a unified regression task, handling both category prediction and bounding box regression within a single network. YOLO surpasses SSD in terms of speed and end-to-end modeling capabilities. In particular, in YOLOv5 and subsequent iterations, enhancements to the network structure and loss function have not only enabled real-time detection, but also significantly improved detection accuracy for small targets and occlusion scenarios. These advances effectively address the need for high-performance and low-latency detectors in practical traffic settings. In recent times, the field of vehicle target detection has faced significant challenges with respect to real-time processing and accuracy in complex environments, which are crucial for intelligent transportation and autonomous driving. To address the constraints of devices with limited resources, MobileNet [29], recognized for its efficiency, is commonly used as a foundational network to create mobile detectors such as MobileNet-YOLO due to its compact size and quick inference abilities. Although MobileNet excels at speed, it encounters challenges in expressing features comprehensively, leading to issues in dealing with intricate scenarios, such as occlusion or detecting small targets. In contrast, the Efficient Detection (EfficientDet) series [30] incorporates pyramidal features in a lightweight manner, achieving a balance between precision and efficiency in vehicle detection. However, its detection accuracy lags behind newer architectures, and it is less sensitive to detecting complex objects. Recently, a Transformer-based Real-Time Detection Transformer (RT-DETR) [31] has emerged, providing improved modeling capabilities and real-time performance. Despite its advantages, the high computational requirements of RT-DETR pose challenges in practical implementation. Consequently, developing a vehicle detection model that considers accuracy, speed, and deployment efficiency remains a complex research pursuit. To improve the performance of vehicle target detection in complex traffic environments and solve the problem that it is difficult to use a lightweight model as a detection model, this article proposes a lightweight vehicle detection model based on enhanced YOLOv8. The model retains the structure of the upper layer of the backbone and introduces the Adaptive Downsampling (ADown) module to improve the convolution structure in the Convolution-BatchNorm-SiLU module (CBS) of the lower layer of the backbone and the convolution structure in the CBS module of the neck. Through Adaptive Downsampling, the computational pressure problem in high-resolution image processing is solved, detailed information of key vehicle targets is effectively retained, and detection accuracy and real-time performance are improved. A Lightweight Shared Convolutional Detection Head (LSCD) is proposed. The shared convolutional layer is designed using group normalization to replace the head convolutional structure, so that the classification, regression, and confidence tasks share the convolutional layer, and the network structure of the original model detection head is reconstructed, which solves the computational bottleneck of accurate and real-time recognition of vehicle targets in complex traffic scenarios and improves the accuracy and speed of vehicle detection. We conducted detailed experiments on two datasets, and the results show that the model is suitable for multitype vehicle target detection in a variety of weather and scenarios.