Road Damage Detection for Multiple Countries

Automatic monitoring of road conditions to detect different road damage types, such as cracks, potholes, etc., at low cost is important for early detection and timely maintenance of roads. This enables road maintenance bodies to perform frequent monitoring trips given the limited availability of allocated funds and manpower. In this direction, road damage detection using image processing techniques and deep learning on wide-view road images captured from smartphones or vehicle-mounted cameras has been actively researched in the past few years. This paper proposes deep learning-based neural networks and explores the feasibility of combining road damage data from different countries for road damage detection. The models were trained on the dataset made available as part of the Crowdsensing-based Road Damage Detection Challenge (CRDDC2022) of IEEE BigData 2022. The trained models were thoroughly evaluated on test datasets. Our proposed approach achieved an average F1 score of 0.628 allowing us to be in the Top 10 of the challenge. F1 scores achieved for Overall, India, Japan, Norway, and the United States were 0.697, 0.493, 0.715, 0.461, and 0.775 respectively.