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Automatic detection and classification of pavement distresses is critical intimely maintaining and rehabilitating pavement surfaces. With the evolution ofdeep learning and high performance computing, the feasibility of vision-basedpavement defect assessments has significantly improved. In this study, theauthors deploy state-of-the-art deep learning algorithms based on differentnetwork backbones to detect and characterize pavement distresses. The influenceof different backbone models such as CSPDarknet53, Hourglass-104 andEfficientNet were studied to evaluate their classification performance. Themodels were trained using 21,041 images captured across urban and rural streetsof Japan, Czech Republic and India. Finally, the models were assessed based ontheir ability to predict and classify distresses, and tested using F1 scoreobtained from the statistical precision and recall values. The best performingmodel achieved an F1 score of 0.58 and 0.57 on two test datasets released bythe IEEE Global Road Damage Detection Challenge. The source code including thetrained models are made available at [1].

Deep Learning Frameworks for Pavement Distress Classification: A Comparative Analysis