期刊
JOURNAL OF COMPUTING IN CIVIL ENGINEERING
卷 33, 期 3, 页码 -出版社
ASCE-AMER SOC CIVIL ENGINEERS
DOI: 10.1061/(ASCE)CP.1943-5487.0000831
关键词
Pavement monitoring; Road crack detection; Deep learning; Convolutional neural network; Black box camera
资金
- Infrastructure and Transportation Technology Promotion Research Program - Ministry of Land, Infrastructure, and Transport of the Korean government [18CTAP-C133290-02]
Cracks cause deterioration of road performance and functional or structural failure if not managed in a timely manner. This paper proposes an automated crack detection method using a car black box camera to address this problem. The proposed method uses a deep learning model [i.e., convolutional neural network (CNN)] composed of segmentation and classification modules. The segmentation process is performed to extract only the road surface in order to remove elements that interfere with crack detection in the black box image. Then, cracks are detected through analysis of patch units within the extracted road surface. The proposed CNN architecture classifies the elements of the road surface into three categories (i.e., crack, road marking, and intact area) with 90.45% accuracy. The results of the proposed CNN architecture are better than those of previous studies. (C) 2019 American Society of Civil Engineers.
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