重庆工商大学学报（自然科学版）2026，Vol.43Issue(3)：38-44,7.DOI:10.16055/j.issn.1672-058X.2026.0003.005
基于改进YOLOv8的车辆检测算法研究
Research on a Vehicle Detection Algorithm Based on Improved YOLOv8
摘要
Abstract
Objective This study addresses the following challenges in vehicle detection under road traffic environments:small target sizes,occlusions,unclear targets,and the consequent false positives and missed detections.Methods An index named ShapeIoU was proposed to assess the matching degree between the target detection box and the real shape of the target.By calculating the similarity of the target contour,the matching degree between the detection box and the real shape was measured to enhance the precision and accuracy of object detection.A convolutional kernel module adjusted by the dynamic snake convolution(DSConv)module was introduced.This module could adaptively adjust the shape of the convolutional kernel according to the changes in the shape and size of the target,so as to capture the features of the target more effectively.To improve the multi-scale detection ability,enhance the detection accuracy and the expression ability of the model,a P2 detection head was added during the improvement.Results The Precision,Recall,mAP@0.5,and mAP@0.5:0.95 of the improved YOLOv8 model algorithm increased by 2.6％,3.2％,3.2％,and 3.0％respectively,reaching 87.9％,84.2％,89.4％,and 65.4％.Conclusion The detection of small,occluded,and unclear targets in complex traffic environments has been significantly improved.It is verified that this improved method can effectively solve problems such as low accuracy in vehicle target detection.关键词
YOLOv8/车辆检测/动态蛇形卷积/ShapeIoU/P2检测头Key words
YOLOv8/vehicle detection/dynamic snake convolution/ShapeIoU/P2 detection head分类
信息技术与安全科学引用本文复制引用
陈争志,刘国巍..基于改进YOLOv8的车辆检测算法研究[J].重庆工商大学学报（自然科学版）,2026,43(3):38-44,7.基金项目
安徽省研究生教育质量工程项目资助(2022CXCYJS016). （2022CXCYJS016）
