阈值
人工智能
卷积神经网络
计算机科学
像素
计算机视觉
人工神经网络
路面
深度学习
分割
图像分割
模式识别(心理学)
图像(数学)
任务(项目管理)
工程类
系统工程
土木工程
作者
Rui Fan,Mohammud Junaid Bocus,Yilong Zhu,Jianhao Jiao,Li Wang,Fulong Ma,Shanshan Cheng,Ming Liu
标识
DOI:10.1109/ivs.2019.8814000
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.
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