Road Damages Detection and Classification with YOLOv7
This work addresses the problem of labor-intensive and unsafe manual road damage inspection for transportation infrastructure maintenance, though it is incremental as it applies existing methods to a new dataset.
The paper tackled automatic road damage detection and classification by collecting data from Google Street View and using YOLOv7 with coordinate attention and fine-tuning techniques, achieving F1 scores of 81.7% on U.S. data and 74.1% on all test images.
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.