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Associate Professor
Supervisor of Doctorate Candidates
Supervisor of Master's Candidates

Alma Mater:The Hong Kong Polytechnic University
Degree:Doctoral Degree
School/Department:Dalian University of Technology,China
Discipline:Computational Mechanics. Aerospace Mechanics and Engineering. Flight Vehicle Design. Materials Physics and Chemistry. Polymer Materials
Business Address:411A, No.1 integrated experimental building, Dalian University of Technology, China
Contact Information:xuhao@dlut.edu.cn
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Current position: Home >> Scientific Research >> Paper Publications

Enhanced Intelligent Identification of Concrete Cracks Using Multi-Layered Image Preprocessing-Aided Convolutional Neural Networks

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Indexed by:Journal Papers

Date of Publication:2020-04-01


Included Journals:PubMed、SCIE



Key Words:concrete crack identification; convolutional neural network; homomorphic filtering; structural health monitoring; signal processing

Abstract:Crack identification plays an essential role in the health diagnosis of various concrete structures. Among different intelligent algorithms, the convolutional neural networks (CNNs) has been demonstrated as a promising tool capable of efficiently identifying the existence and evolution of concrete cracks by adaptively recognizing crack features from a large amount of concrete surface images. However, the accuracy as well as the versatility of conventional CNNs in crack identification is largely limited, due to the influence of noise contained in the background of the concrete surface images. The noise originates from highly diverse sources, such as light spots, blurs, surface roughness/wear/stains. With the aim of enhancing the accuracy, noise immunity, and versatility of CNN-based crack identification methods, a framework of enhanced intelligent identification of concrete cracks is established in this study, based on a hybrid utilization of conventional CNNs with a multi-layered image preprocessing strategy (MLP), of which the key components are homomorphic filtering and the Otsu thresholding method. Relying on the comparison and fine-tuning of classic CNN structures, networks for detection of crack position and identification of crack type are built, trained, and tested, based on a dataset composed of a large number of concrete crack images. The effectiveness and efficiency of the proposed framework involving the MLP and the CNN in crack identification are examined by comparative studies, with and without the implementation of the MLP strategy. Crack identification accuracy subject to different sources and levels of noise influence is investigated.