Abstract
Poorly maintained roads can cause lethal automobile accidents in various ways. Thus, detecting and reporting damaged parts of roads is one of the most crucial road maintenance tasks, and it is vital to identify the type and severity of the damage to help fix it as soon as possible. Several researchers have used computer vision and detection algorithms to detect and classify road damages, including cracking, distortion, and disintegration. Providing automatic road damage detection methods can help municipalities save time and effort and speed up maintenance operations. This study proposes a method to classify road damage and its severity based on CNN and trained on a newly curated dataset collected from Saudi roads. Hence, this study also presents a dataset with labeled classes, which are cracks, potholes, depressions, and shoving. The dataset was collected in collaboration with maintenance employees in the municipality of Rabigh Governorate using a smartphone device and reviewed by experts. In addition, several deep learning algorithms were implemented and evaluated using the proposed dataset. The study found that the proposed custom CNN (RoadNet) has higher accuracy than pre-trained models.
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference53 articles.
1. Evaluating the nature of distractive driving factors towards road traffic accident;Civ. Eng. J.,2020
2. Ministry of Transport and Logistic Services (2022, September 15). Road Maintenance, Available online: https://mot.gov.sa/en/Roads/Pages/RoadsMaintenance.aspx.
3. Department of Local Infrastructure Development and Agricultural Roads (DoLIDAR) (2016, March 01). Road Maintenance Groups (RMG). Available online: https://www.ilo.org/dyn/asist/docs/F1259778069/rmg.pdf.
4. Road damage detection and classification using deep neural networks with smartphone images;Comput.-Aided Civ. Infrastruct. Eng.,2018
5. RDD2020: An annotated image dataset for automatic road damage detection using deep learning;Data Brief,2021
Cited by
6 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献