Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding

被引:0
|
作者
Fan, Rui [1 ]
Bocus, Mohammud Junaid [2 ]
Zhu, Yilong [3 ]
Jiao, Jianhao [1 ]
Wang, Li [4 ]
Ma, Fulong [3 ]
Cheng, Shanshan [4 ]
Liu, Ming [1 ]
机构
[1] Kong Univ Sci & Technol, Robot Inst, Robot & Multipercept Lab, Hong Kong, Peoples R China
[2] Univ Bristol, Visual Informat Inst, Bristol, Avon, England
[3] Unity Dr Technol Inc, Shenzhen, Peoples R China
[4] Natl Engn Res Ctr Rd Maintenance Technol, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
ALGORITHM;
D O I
10.1109/ivs.2019.8814000
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Crack is one of the most common road distresses which may pose road safety hazards. Generally, crack detection is performed by either certified inspectors or structural engineers. This task is, however, time-consuming, subjective and labor-intensive. In this paper, a novel road crack detection algorithm which is based on deep learning and adaptive image segmentation is proposed. Firstly, a deep convolutional neural network is trained to determine whether an image contains cracks or not. The images containing cracks are then smoothed using bilateral filtering, which greatly minimizes the number of noisy pixels. Finally, cracks are extracted from the road surface using an adaptive thresholding method. The experimental results illustrate that our network can classify images with an accuracy of 99.92%, and the cracks can be successfully extracted from the images using our proposed thresholding algorithm.
引用
收藏
页码:474 / 479
页数:6
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