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Article

Civil Infrastructure Damage and Corrosion Detection: An Application of Machine Learning

1
School of Built Environment, University of New South Wales, Sydney, NSW 2052, Australia
2
School of Surveying and Built Environment, University of Southern Queensland, Springfield, QLD 4300, Australia
3
Department of Visual Computing, University of Saarland, 66123 Saarbrücken, Germany
4
School of Computer Science, The University of Sydney, Sydney, NSW 2006, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Łukasz Sadowski
Buildings 2022, 12(2), 156; https://doi.org/10.3390/buildings12020156
Received: 7 December 2021 / Revised: 29 January 2022 / Accepted: 31 January 2022 / Published: 1 February 2022
Automatic detection of corrosion and associated damages to civil infrastructures such as bridges, buildings, and roads, from aerial images captured by an Unmanned Aerial Vehicle (UAV), helps one to overcome the challenges and shortcomings (objectivity and reliability) associated with the manual inspection methods. Deep learning methods have been widely reported in the literature for civil infrastructure corrosion detection. Among them, convolutional neural networks (CNNs) display promising applicability for the automatic detection of image features less affected by image noises. Therefore, in the current study, we propose a modified version of deep hierarchical CNN architecture, based on 16 convolution layers and cycle generative adversarial network (CycleGAN), to predict pixel-wise segmentation in an end-to-end manner using the images of Bolte Bridge and sky rail areas in Victoria (Melbourne). The convolutedly designed model network proposed in the study is based on learning and aggregation of multi-scale and multilevel features while moving from the low convolutional layers to the high-level layers, thus reducing the consistency loss in images due to the inclusion of CycleGAN. The standard approaches only use the last convolutional layer, but our proposed architecture differs from these approaches and uses multiple layers. Moreover, we have used guided filtering and Conditional Random Fields (CRFs) methods to refine the prediction results. Additionally, the effectiveness of the proposed architecture was assessed using benchmarking data of 600 images of civil infrastructure. Overall, the results show that the deep hierarchical CNN architecture based on 16 convolution layers produced advanced performances when evaluated for different methods, including the baseline, PSPNet, DeepLab, and SegNet. Overall, the extended method displayed the Global Accuracy (GA); Class Average Accuracy (CAC); mean Intersection Of the Union (IOU); Precision (P); Recall (R); and F-score values of 0.989, 0.931, 0.878, 0.849, 0.818 and 0.833, respectively. View Full-Text
Keywords: artificial intelligence; building corrosion detection; building damage detection; civil infrastructure crack detection; civil infrastructure inspection; image processing; machine learning; unmanned aerial vehicles artificial intelligence; building corrosion detection; building damage detection; civil infrastructure crack detection; civil infrastructure inspection; image processing; machine learning; unmanned aerial vehicles
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MDPI and ACS Style

Munawar, H.S.; Ullah, F.; Shahzad, D.; Heravi, A.; Qayyum, S.; Akram, J. Civil Infrastructure Damage and Corrosion Detection: An Application of Machine Learning. Buildings 2022, 12, 156. https://doi.org/10.3390/buildings12020156

AMA Style

Munawar HS, Ullah F, Shahzad D, Heravi A, Qayyum S, Akram J. Civil Infrastructure Damage and Corrosion Detection: An Application of Machine Learning. Buildings. 2022; 12(2):156. https://doi.org/10.3390/buildings12020156

Chicago/Turabian Style

Munawar, Hafiz Suliman, Fahim Ullah, Danish Shahzad, Amirhossein Heravi, Siddra Qayyum, and Junaid Akram. 2022. "Civil Infrastructure Damage and Corrosion Detection: An Application of Machine Learning" Buildings 12, no. 2: 156. https://doi.org/10.3390/buildings12020156

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