基于YOLO算法深度学习的路面病害检测方法研究

Researchon Pavement Disease Detection Method Based on YOLO Algorithm Deep Learning

提出一种基于深度学习的路面病害检测方法,采用了YOLOv3算法对道路表面的裂缝、坑洼等病害进行快速准确地识别和分类。详细介绍了YOLOv3算法的结构和工作原理,设计了损失函数,以优化模型在分类、定位及置信度评估方面的表现,创建了一个增补的道路表面损伤检测数据集,以适应中国乡镇及社区街道的具体情况。最后,通过在实际道路条件下的测试验证,YOLOv3算法在路面病害检测上显示出91.0%~97.3%的高准确率,证明了基于深度学习的路面病害检测技术在实际应用中的可行性和有效性。

A deep learning-based pavement damage detection method is proposed,and the YOLOv3 algorithm is adopted to quickly and accurately recognize and classify the cracks,potholes and other diseases on the road surface.The structure and working principle of the YOLOv3 algorithm are described in detail,the loss function is designed to optimize the performance of the model in classification,localization,and confidence assessment,and a complementary road surface damage detection dataset is created to adapt to the specific conditions of Chinese townships and community streets.Finally,the YOLOv3 algorithm shows a high accuracy of 91.0%to 97.3%on road surface damage detection through testing and validation under real road conditions,demonstrating the feasibility and effectiveness of deep learning-based road surfacedamagedetection technology in practical applications.