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Open AccessArticle

Vision-Based Autonomous Crack Detection of Concrete Structures Using a Fully Convolutional Encoder–Decoder Network

School of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, Korea
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Sensors 2019, 19(19), 4251; https://doi.org/10.3390/s19194251
Received: 24 August 2019 / Revised: 19 September 2019 / Accepted: 28 September 2019 / Published: 30 September 2019
(This article belongs to the Special Issue Sensors Fusion in Non-Destructive Testing Applications)
The visual inspection of massive civil infrastructure is a common trend for maintaining its reliability and structural health. However, this procedure, which uses human inspectors, requires long inspection times and relies on the subjective and empirical knowledge of the inspectors. To address these limitations, a machine vision-based autonomous crack detection method is proposed using a deep convolutional neural network (DCNN) technique. It consists of a fully convolutional neural network (FCN) with an encoder and decoder framework for semantic segmentation, which performs pixel-wise classification to accurately detect cracks. The main idea is to capture the global context of a scene and determine whether cracks are in the image while also providing a reduced and essential picture of the crack locations. The visual geometry group network (VGGNet), a variant of the DCCN, is employed as a backbone in the proposed FCN for end-to-end training. The efficacy of the proposed FCN method is tested on a publicly available benchmark dataset of concrete crack images. The experimental results indicate that the proposed method is highly effective for concrete crack classification, obtaining scores of approximately 92% for both the recall and F1 average. View Full-Text
Keywords: structural health monitoring; image processing; computer vision; deep learning; concrete structure crack detection; visual geometry group network; semantic segmentation structural health monitoring; image processing; computer vision; deep learning; concrete structure crack detection; visual geometry group network; semantic segmentation
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Islam, M.M.M.; Kim, J.-M. Vision-Based Autonomous Crack Detection of Concrete Structures Using a Fully Convolutional Encoder–Decoder Network. Sensors 2019, 19, 4251.

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