Road defect detection and automated driving risk assessment using binocular vision
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.
国家科技期刊平台
NSTL
万方数据
