A Depth Camera-Based Intelligent Method for Identifying and Quantifying Pavement Diseases
In this study, a depth camera-based intelligence method is proposed. First, road damage images are collected and transformed into a training set. Then training, defect detection, defect extraction, and classification are performed. In addition, a YOLOv5 is used to create, train, validate, and test t...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/4992321 |
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| Summary: | In this study, a depth camera-based intelligence method is proposed. First, road damage images are collected and transformed into a training set. Then training, defect detection, defect extraction, and classification are performed. In addition, a YOLOv5 is used to create, train, validate, and test the label database. The method does not require a predetermined distance between the measurement target and the sensor; can be applied to moving scenes; and is important for the detection, classification, and quantification of pavement diseases. The results show that the sensor can achieve plane fitting at investigated working distances by means of a deep learning network. In addition, two pavement examples show that the detection method can save a lot of manpower and improve the detection efficiency with certain accuracy. |
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| ISSN: | 1687-8094 |