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.

翻译：维护道路基础设施是维持安全、经济和可持续运输系统的重要因素之一。人工道路损坏数据收集是艰苦和不安全的,供人类使用。这一领域将受益于人工智能技术的快速推进和传播。具体地说,深层次的学习进步使得能够从收集的道路图像中自动探测道路损坏情况。这项工作提议利用谷歌街视图来收集和标签道路损坏数据,并使用YOLOv7 (《你只看一次》第七版),同时协调关注和相关的精确度微调技术,如标签顺畅和共同方法,以培训用于自动道路损坏探测和分类的深层学习模型。拟议的方法将适用于基于人群的交通损坏探测挑战(CRDC2022)、IEEE BIData 2022。结果显示,从谷歌街视图收集的数据是有效的,拟议深层学习方法的结果是,从美国收集的公路损坏数据中有81.7%的F1分,使用谷街视图收集,74.1%的这一数据集的所有测试图像。