Deep learning based automated segmentation of air-void system in hardened concrete surface using three dimensional reconstructed images
Deep learning based automated segmentation of air-void system in hardened concrete surface using three dimensional reconstructed images
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DOI：
10.1016/j.conbuildmat.2022.126717
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发表时间：
2022-02-04
影响因子：
7.4
通讯作者：
Liu, Jinli
中科院分区：
文献类型：
--
作者：
Tao, Jueqiang;Gong, Haitao;Liu, Jinli
在ASTM C457中规定的自动空气空隙检测方法需要借助于对比度增强，这是耗时且劳动密集的。本研究探讨利用三维（3D）重建和深度卷积神经网络（DCNN）的方法来检测硬化混凝土表面的空气空隙，而不使用对比度增强。实验结果表明，DCNN可以准确区分硬化混凝土图像中的气泡，检测精度超过0.9，仅需不到一分钟。空气含量、比表面积和间距因子的准确率分别为0.92、0.91和0.89。
The automated air-void detection methods specified in the ASTM C457 require the aid of contrast enhancement which is time consuming and labor intensive. This study investigated the utilization of three-dimensional (3D) reconstruction and Deep Convolution Neural Network (DCNN) methods to detect the air voids in hardened concrete surfaces without the use of contrast enhancement. The experimental results showed that the DCNN could accurately distinguish air voids from hardened concrete images with the detection accuracy of over 0.9 in only less than a minute. The accuracy rates for air content, specific surface, and spacing factor were 0.92, 0.91, and 0.89, respectively.