  Maintaining the roadway infrastructure is one of the essential factors in
enabling a safe, economic, and sustainable transportation system. Manual
roadway damage data collection is laborious and unsafe for humans to perform.
This area is poised to benefit from the rapid advance and diffusion of
artificial intelligence technologies. Specifically, deep learning advancements
enable the detection of road damages automatically from the collected road
images. This work proposes to collect and label road damage data using Google
Street View and use YOLOv7 (You Only Look Once version 7) together with
coordinate attention and related accuracy fine-tuning techniques such as label
smoothing and ensemble method to train deep learning models for automatic road
damage detection and classification. The proposed approaches are applied to the
Crowdsensing-based Road Damage Detection Challenge (CRDDC2022), IEEE BigData
2022. The results show that the data collection from Google Street View is
efficient, and the proposed deep learning approach results in F1 scores of
81.7% on the road damage data collected from the United States using Google
Street View and 74.1% on all test images of this dataset.
