Threshold-based BRISQUE-assisted deep learning for enhancing crack detection in concrete structures
Pennada, Sanjeetha and Perry, Marcus and McAlorum, Jack and Dow, Hamish and Dobie, Gordon (2023) Threshold-based BRISQUE-assisted deep learning for enhancing crack detection in concrete structures. Journal of Imaging, 9 (10). 218. ISSN 2313-433X (https://doi.org/10.3390/jimaging9100218)
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Abstract
Automated visual inspection has made significant advancements in the detection of cracks on the surfaces of concrete structures. However, low-quality images significantly affect the classification performance of convolutional neural networks (CNNs). Therefore, it is essential to evaluate the suitability of image datasets used in deep learning models, like Visual Geometry Group 16 (VGG16), for accurate crack detection. This study explores the sensitivity of the BRISQUE method to different types of image degradations, such as Gaussian noise and Gaussian blur. By evaluating the performance of the VGG16 model on these degraded datasets with varying levels of noise and blur, a correlation is established between image degradation and BRISQUE scores. The results demonstrate that images with lower BRISQUE scores achieve higher accuracy, F1 score, and Matthew’s correlation coefficient (MCC) in crack classification. The study proposes the implementation of a BRISQUE score threshold (BT) to optimise training and testing times, leading to reduced computational costs. These findings have significant implications for enhancing accuracy and reliability in automated visual inspection systems for crack detection and structural health monitoring (SHM).
ORCID iDs
Pennada, Sanjeetha, Perry, Marcus ORCID: https://orcid.org/0000-0001-9173-8198, McAlorum, Jack ORCID: https://orcid.org/0000-0001-8348-9945, Dow, Hamish ORCID: https://orcid.org/0000-0002-1431-7063 and Dobie, Gordon ORCID: https://orcid.org/0000-0003-3972-5917;-
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Item type: Article ID code: 86920 Dates: DateEvent10 October 2023Published3 October 2023Accepted21 August 2023SubmittedSubjects: Technology > Engineering (General). Civil engineering (General) Department: Faculty of Engineering > Civil and Environmental Engineering
Faculty of Engineering > Electronic and Electrical EngineeringDepositing user: Pure Administrator Date deposited: 11 Oct 2023 12:32 Last modified: 01 Oct 2024 00:43 URI: https://strathprints.strath.ac.uk/id/eprint/86920