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Automatic pavement distress severity detection using deep learning
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AI
DOI：10.1080/14680629.2023.2276422.png)
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摘要
En 中文
Roads are one of the most critical infrastructures, which should be maintained at a high quality of service. For this purpose, road pavement should be assessed cost-effectively. In the past, image processing methods were used to analyze pavement conditions. In recent years, machine learning methods have been employed, while now deep learning methods are applied. Deep learning has outperformed other methods regarding the accuracy and speed of pavement distress evaluation. In this research, a deep learning algorithm called YOLOv5 is deployed to detect pavement block cracking and estimate its severity using images taken from the right of way via a road surface profiler. Two models are successfully trained and tested, one to detect block cracking and the other to predict its severity with a sufficient level of accuracy of 84.5% and 76.6%, respectively. It is concluded that the model not only can detect block cracking but also predict its severity.
Keyword:
Pavement management system
deep learning
block cracking
object detection
YOLO
annotation
期刊
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3
论文数:
2.6K
被引数:
6.9K
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引用论文
Automated Pavement Distress Detection and Deterioration Analysis Using Street View Map.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
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