A Novel Multi-Scale Feature Fusion-Based 3SCNet for Building Crack Detection

Dhirendra Prasad Yadav, Kamal Kishore, Ashish Gaur, Ankit Kumar, Kamred Udham Singh, Teekam Singh and Chetan Swarup ()
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Dhirendra Prasad Yadav: Department of Computer Engineering & Applications, GLA University, Mathura 281406, Uttar Pradesh, India
Kamal Kishore: Advanced Construction Engineering Research Center, Department of Civil Engineering, GLA University, Mathura 281406, Uttar Pradesh, India
Ashish Gaur: Department of Computer Engineering & Applications, GLA University, Mathura 281406, Uttar Pradesh, India
Ankit Kumar: Department of Computer Engineering & Applications, GLA University, Mathura 281406, Uttar Pradesh, India
Kamred Udham Singh: Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan
Teekam Singh: School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, Uttarakhand, India
Chetan Swarup: Department of Basic Science, College of Science and Theoretical Studies, Saudi Electronic University, Riyadh-Male Campus, Riyadh 13316, Saudi Arabia

Sustainability, 2022, vol. 14, issue 23, 1-16

Abstract: Crack detection at an early stage is necessary to save people’s lives and to prevent the collapse of building/bridge structures. Manual crack detection is time-consuming, especially when a building structure is too high. Image processing, machine learning, and deep learning-based methods can be used in such scenarios to build an automatic crack detection system. This study uses a novel deep convolutional neural network, 3SCNet (3ScaleNetwork), for crack detection. The SLIC (Simple Linear Iterative Clustering) segmentation method forms the cluster of similar pixels and the LBP (Local Binary Pattern) finds the texture pattern in the crack image. The SLIC, LBP, and grey images are fed to 3SCNet to form pool of feature vector. This multi-scale feature fusion (3SCNet+LBP+SLIC) method achieved the highest sensitivity, specificity, an accuracy of 99.47%, 99.75%, and 99.69%, respectively, on a public historical building crack dataset. It shows that using SLIC super pixel segmentation and LBP can improve the performance of the CNN (Convolution Neural Network). The achieved performance of the model can be used to develop a real-time crack detection system.

Keywords: building crack; LBP; SLIC; deep CNN; classification (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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