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Sensors 2018, 18(9), 3042; https://doi.org/10.3390/s18093042

Pixel-Wise Crack Detection Using Deep Local Pattern Predictor for Robot Application

1
School of Electronic and Information Engineering, North China University of Technology, Beijing 100144, China
2
Unmanned Systems Research Institute, Beihang University, Beijing 100191, China
3
School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
*
Author to whom correspondence should be addressed.
Received: 14 July 2018 / Revised: 23 August 2018 / Accepted: 7 September 2018 / Published: 11 September 2018
(This article belongs to the Special Issue Semantic Representations for Behavior Analysis in Robotic system)
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Abstract

Robotic vision-based crack detection in concrete bridges is an essential task to preserve these assets and their safety. The conventional human visual inspection method is time consuming and cost inefficient. In this paper, we propose a robust algorithm to detect cracks in a pixel-wise manner from real concrete surface images. In practice, crack detection remains challenging in the following aspects: (1) detection performance is disturbed by noises and clutters of environment; and (2) the requirement of high pixel-wise accuracy is difficult to obtain. To address these limitations, three steps are considered in the proposed scheme. First, a local pattern predictor (LPP) is constructed using convolutional neural networks (CNN), which can extract discriminative features of images. Second, each pixel is efficiently classified into crack categories or non-crack categories by LPP, using as context a patch centered on the pixel. Lastly, the output of CNN—i.e., confidence map—is post-processed to obtain the crack areas. We evaluate the proposed algorithm on samples captured from several concrete bridges. The experimental results demonstrate the good performance of the proposed method. View Full-Text
Keywords: local pattern predictor; crack detection; bridge inspection; convolutional neural networks; robotic vision local pattern predictor; crack detection; bridge inspection; convolutional neural networks; robotic vision
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Li, Y.; Li, H.; Wang, H. Pixel-Wise Crack Detection Using Deep Local Pattern Predictor for Robot Application. Sensors 2018, 18, 3042.

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