Crack Detection in Masonry Structures using Convolutional Neural Networks and Support Vector Machines

Crack Detection in Masonry Structures using Convolutional Neural Networks and Support Vector Machines Krisada Chaiyasarn, Wasif Khan, Luqman Ali, Mayank Sharma, Daniel Brackenbury and Matthew Dejong Pages 118-125 (2018 Proceedings of the 35th ISARC, Berlin, Germany, ISBN 978-3-00-060855-1) Abstract: Masonry structures in historical sites are deteriorating due to ageing and man-made activities. Regular inspection and maintenance work is required to ensure the structural integrity of historic structures. The inspection work is typically carried out by visual inspection, which is costly and laborious, and yields to subjective results. In this study, an automatic image-based crack detection system for masonry structures is proposed to aid the inspection procedure. Previous crack detection systems generally involve the extraction of hand crafted features, which are classified by classification algorithms. Such approach relies heavily on feature vectors and may fail as some hidden features may not be extracted. In this study, we propose a crack detection system which combines deep Convolutional Neural Networks (CNN) and Support Vector Machines (SVM). CNN is used in extracting features from RGB images and SVM is used as an alternative classifier to a softmax layer to enhance the classification ability. A dataset containing images of cracks from masonry structures was created using a digital camera and an unmanned aerial vehicle from historical sites. The images were used for training and validating the proposed system. It is shown that the combined CNN and SVM model performs better than the model using CNN alone with the detection accuracy of approximately 86% in the validation images. It is also shown that the system can be used to detect cracks automatically for the images of masonry structures, which is useful for inspection of heritage structures. Keywords: Convolutional Neural Network, Support Vector Machine, Masonry structures, Crack detection, Computer vision DOI: https://doi.org/10.22260/ISARC2018/0016 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley