论文标题
路面遇险分类的深度学习框架：比较分析
Deep Learning Frameworks for Pavement Distress Classification: A Comparative Analysis
论文作者
论文摘要
对及时维护和修复人行道表面的自动检测和分类至关重要。随着深度学习和高性能计算的发展，基于视觉的路面缺陷评估的可行性已大大改善。在这项研究中，作者基于不同的网络骨架来部署最先进的深度学习算法，以检测和表征路面困扰。研究了不同骨干模型（例如CSPDarknet53，Hourglass-104和ExtricNet）的影响，以评估其分类性能。使用21,041张图像在日本，捷克共和国和印度的城市和农村街道上捕获的21,041张图像进行了培训。最后，根据其预测和分类困扰的能力来评估模型，并使用从统计精度和召回值获得的F1得分进行了测试。最佳性能模型在IEEE全球道路损伤检测挑战发布的两个测试数据集上达到了0.58和0.57的F1得分。包括训练有素的型号的源代码可在[1]上提供。
Automatic detection and classification of pavement distresses is critical in timely maintaining and rehabilitating pavement surfaces. With the evolution of deep learning and high performance computing, the feasibility of vision-based pavement defect assessments has significantly improved. In this study, the authors deploy state-of-the-art deep learning algorithms based on different network backbones to detect and characterize pavement distresses. The influence of different backbone models such as CSPDarknet53, Hourglass-104 and EfficientNet were studied to evaluate their classification performance. The models were trained using 21,041 images captured across urban and rural streets of Japan, Czech Republic and India. Finally, the models were assessed based on their ability to predict and classify distresses, and tested using F1 score obtained from the statistical precision and recall values. The best performing model achieved an F1 score of 0.58 and 0.57 on two test datasets released by the IEEE Global Road Damage Detection Challenge. The source code including the trained models are made available at [1].
