计算机工程与科学2026，Vol.48Issue(3)：561-570,10.DOI:10.3969/j.issn.1007-130X.2026.03.018
基于改进YOLOv8的道路障碍物检测模型
A road obstacle detection model based on improved YOLOv8
蒋建伟 1贾小云 1段克盼 1郭宇 1盛良浩 1魏联婷1
作者信息
- 1. 陕西科技大学电子信息与人工智能学院,陕西 西安 710021
- 折叠
摘要
Abstract
Road obstacle detection is a significant part of intelligent driving technology.In response to the current problems of low accuracy in detecting small obstacles on roads,poor detection perform-ance in adverse environmental scenes,and scarcity of road obstacle datasets,a suitable obstacle dataset for road scenes is organized and constructed.Based on the YOLOv8 model,a new model,YOLOv8-J with high detection accuracy is proposed.Firstly,a lightweight backbone network called LskViT based on RepViT is designed to enhance the model's ability to extract multi-scale features.Secondly,the SPD-Conv convolutional module is introduced to strengthen the model's learning capability for low-resolution ima-ges.Finally,an additional small object detection layer is added to help the model capture more shallow features,thereby improving its detection performance for small obstacles.Experimental results demon-strate that,compared to the baseline model YOLOv8,the improved YOLOv8-J model achieves increa-ses of 5.9 percentage points and 6.1 percentage points in mAP@0.5 and mAP@0.5:0.95 values,respectively.The improved model is well-suited for road obstacle detection tasks and further enhances detection performance for small obstacles in adverse environments.关键词
道路障碍物/注意力机制/卷积模块/模型优化/YOLOv8模型Key words
road obstacle/attention mechanism/convolutional module/model optimization/YOLOv8 model分类
信息技术与安全科学引用本文复制引用
蒋建伟,贾小云,段克盼,郭宇,盛良浩,魏联婷..基于改进YOLOv8的道路障碍物检测模型[J].计算机工程与科学,2026,48(3):561-570,10.
