论文标题
Yolov7的道路损坏检测和分类
Road Damages Detection and Classification with YOLOv7
论文作者
论文摘要
维护道路基础设施是实现安全，经济和可持续运输系统的基本因素之一。手动道路损坏数据收集是费力的，不安全的人类表演。该领域有望受益于人工智能技术的快速发展和扩散。具体而言，深度学习进步可以自动从收集的道路图像中发现道路损失。这项工作建议使用Google Street View收集和标记道路损坏数据，并使用Yolov7（您只看一次版本7），以及协调关注和相关的精确性微调技术，例如标签平滑和合奏方法，以训练深度学习模型以自动道路损害检测和分类。提出的方法适用于基于人群的道路损害检测挑战（CRDDC2022），IEEE BIGDATA 2022。结果表明，Google Street View的数据收集效率很高，拟议的深度学习方法导致F1在使用Google Street View and 74.1 test Anly Test Anly Test As the All test Asstas的F1损害数据中，F1得分为81.7％。
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.
