A Comparative Study on Crack Detection in Concrete Walls Using Transfer Learning Techniques

作者：Remya Elizabeth Philip, A. Diana Andrushia, Anand Nammalvar, Beulah Gnana Ananthi Gurupatham, Krishanu Roy · 发表于：Journal of Composites Science · 年份：2023 · DOI：10.3390/jcs7040169 · 被引用次数：68 · 研究领域：Infrastructure Maintenance and Monitoring、Concrete Corrosion and Durability、Structural Health Monitoring Techniques

Structural cracks have serious repercussions on the safety, adaptability, and longevity of structures. Therefore, assessing cracks is an important parameter when evaluating the quality of concrete construction. As numerous cutting-edge automated inspection systems that exploit cracks have been developed, the necessity for individual/personal onsite inspection has reduced exponentially. However, these methods need to be improved in terms of cost efficiency and accuracy. The deep-learning-based assessment approaches for structural systems have seen a significant development noticed by the structural health monitoring (SHM) community. Convolutional neural networks (CNNs) are vital in these deep learning methods. Technologies such as convolutional neural networks hold promise for precise and accurate condition evaluation. Moreover, transfer learning enables users to use CNNs without needing a comprehensive grasp of algorithms or the capability to modify pre-trained networks for particular purposes. Within the context of this study, a thorough analysis of well-known pre-trained networks for classifying the cracks in buildings made of concrete is conducted. The classification performance of convolutional neural network designs such as VGG16, VGG19, ResNet 50, MobileNet, and Xception is compared to one another with the concrete crack image dataset. It is identified that the ResNet50-based classifier provided accuracy scores of 99.91% for training and 99.88% for testing. Xception arc...