Civil infrastructure damage and corrosion detection: an application of machine learning

Munawar, Hafiz Suliman and Ullah, Fahim ORCID: https://orcid.org/0000-0002-6221-1175 and Shahzad, Danish and Heravi, Amirhossein ORCID: https://orcid.org/0000-0003-2939-7745 and Qayyum, Siddra and Akram, Junaid (2022) Civil infrastructure damage and corrosion detection: an application of machine learning. Buildings, 12 (2):156.

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Abstract

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.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Surveying and Built Environment (1 Jan 2022 -)
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Surveying and Built Environment (1 Jan 2022 -)
Date Deposited: 28 Feb 2022 23:50
Last Modified: 28 Feb 2022 23:52
Uncontrolled Keywords: artificial intelligence; building corrosion detection; building damage detection; civil infrastructure crack detection; civil infrastructure inspection; image processing; machine learning; unmanned aerial vehicles
Fields of Research (2008): 09 Engineering > 0905 Civil Engineering > 090502 Construction Engineering
12 Built Environment and Design > 1202 Building > 120201 Building Construction Management and Project Planning
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080106 Image Processing
Fields of Research (2020): 33 BUILT ENVIRONMENT AND DESIGN > 3302 Building > 330202 Building construction management and project planning
46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460306 Image processing
40 ENGINEERING > 4005 Civil engineering > 400508 Infrastructure engineering and asset management
33 BUILT ENVIRONMENT AND DESIGN > 3302 Building > 330201 Automation and technology in building and construction
40 ENGINEERING > 4005 Civil engineering > 400504 Construction engineering
Identification Number or DOI: https://doi.org/10.3390/buildings12020156
URI: http://eprints.usq.edu.au/id/eprint/46967

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