重庆理工大学学报2024，Vol.38Issue(17)：67-74,8.DOI:10.3969/j.issn.1674-8425(z).2024.09.008
采用双目视觉的道路缺陷检测与自动驾驶风险评估
Road defect detection and automated driving risk assessment using binocular vision
摘要
Abstract
Road defect detection is crucial to ensure the safety of autonomous driving.However,current road defect detection methods fail to meet the demand of automatic driving to accurately detect road defects.Moreover,existing evaluation models primarily consider the severity of road defects without taking the impact of defect distance into account.To address these issues,this paper proposes an approach for road defect detection and autonomous driving risk assessment using improved YOLOv8.The attention mechanism is incorporated at different network positions.The optimal model for autonomous driving scenarios is identified,improving by 1.31％ in mean average precision compared to the original network.Furthermore,our approach enables real-time detection of road defect types and distances.Based on the detection and evaluation results,the risk level for autonomous driving on defect roads is determined and a fundamental strategy for shunning road defects is formulated.关键词
道路缺陷/自动驾驶/神经网络/注意力机制/双目视觉Key words
road defect/autonomous driving/neural network/attention mechanism/binocular vision分类
信息技术与安全科学引用本文复制引用
潘明章,袁乐艺,万振华,梁璐,苟轩源,曹鑫鑫..采用双目视觉的道路缺陷检测与自动驾驶风险评估[J].重庆理工大学学报,2024,38(17):67-74,8.基金项目
国家自然科学基金区域联合重点项目(U23A202599) （U23A202599）
广西大学甘蔗专项科研项目(2022GZB008) （2022GZB008）
