Exploitation of deep learning in the automatic detection of cracks on paved roads
| Autor: | Baoxin Hu, Jianguo Wang, Faizaan Naveed, Ningyuan Li, Won Mo Jung |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
050210 logistics & transportation
business.industry Deep learning 05 social sciences Geography Planning and Development Machine learning computer.software_genre 01 natural sciences Convolutional neural network 010309 optics 0502 economics and business 0103 physical sciences Research article Artificial intelligence business computer Earth-Surface Processes |
| Zdroj: | Geomatica. 73:29-44 |
| ISSN: | 1925-4296 1195-1036 |
| DOI: | 10.1139/geomat-2019-0008 |
| Popis: | With the advance of deep learning networks, their applications in the assessment of pavement conditions are gaining more attention. A convolutional neural network (CNN) is the most commonly used network in image classification. In terms of pavement assessment, most existing CNNs are designed to only distinguish between cracks and non-cracks. Few networks classify cracks in different levels of severity. Information on the severity of pavement cracks is critical for pavement repair services. In this study, the state-of-the-art CNN used in the detection of pavement cracks was improved to localize the cracks and identify their distress levels based on three categories (low, medium, and high). In addition, a fully convolutional network (FCN) was, for the first time, utilized in the detection of pavement cracks. These designed architectures were validated using the data acquired on four highways in Ontario, Canada, and compared with the ground truth that was provided by the Ministry of Transportation of Ontario (MTO). The results showed that with the improved CNN, the prediction precision on a series of test image patches were 72.9%, 73.9%, and 73.1% for cracks with the severity levels of low, medium, and high, respectively. The precision for the FCN was tested on whole pavement images, resulting in 62.8%, 63.3%, and 66.4%, respectively, for cracks with the severity levels of low, medium, and high. It is worth mentioning that the ground truth contained some uncertainties, which partially contributed to the relatively low precision. |
| Databáze: | OpenAIRE |
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