Based on Improved Lightweight YOLOv8 for Vehicle Detection

Abstract

In urban road traffic, vehicle detection in intelligent transportation system can effectively improve road traffic operation efficiency and road safety. Based on this goal, this paper is based on the YOLOv8 framework, using the improved lightweight YOLOv8 model to realize vehicle detection, the method replaces the Backbone network in the YOLOv8n model with a more efficient and lightweight EfficientViT network, and further adds the CBAM attention mechanism in Neck to enhance the detection precision, and later, Conv is replaced with the GhostConv model in Head to reduce the number of parameters and increase the speed of detection. This model greatly improves the detection precision and efficiency. We validate our approach on the UA-DETRAC vehicle detection dataset, and the experimental results show that the proposed detection model Precision reaches 91.17%, mAP@0.5 reaches 75.45%, and Recall reaches about 70.01%. Compared with the original YOLOv8n model, Precision improves by 4.6%, mAP@0.5 improves by about 4.3%, and Recall improves by about 9.1%. Through experimental validation, the model performs well in detecting various complex road traffic scenarios, and this innovative approach can provide strong support for the development of intelligent transportation systems, such as deployed on devices with limited mobile hardware resources, and is expected to make further breakthroughs in future applications.