Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Structural crack detection using deep convolutional neural networks
Abstract Convolutional Neural Networks (CNN) have immense potential to solve a broad range of computer vision problems. It has achieved encouraging results in numerous applications of engineering, medical, and other research fields due to the advancement in hardware, data collection procedures, and efficient algorithms. These innovations have changed the way how specific problems are solved as compared to conventional methods. This article presents a review of CNN implementation on civil structure crack detection. The review highlights the significant research that has been performed to detect structure cracks through classification and segmentation of crack images with CNN in the perspective of image pre-processing techniques, processing hardware, software tools, datasets, network architectures, learning procedures, loss functions, and network performance. The key contribution of this review article is the study and analysis of the most recent developments on crack detection using CNN. Additionally, this work also presents a discussion on crack detection through a manual process, image processing techniques, and machine learning methods along with their limitations. Finally, this article aims for assisting the readers to understand the motivation and methodology of the various CNN-based crack detection methods and to invoke them for exploring the solutions of challenges outlined in future research.
Highlights The recent crack detection methods have adopted Convolutional Neural Networks for crack classification and segmentation. Manual crack inspection, IPT's and other traditional ML methods and techniques have several limitations and drawbacks. The performance of encoder-decoder architectures has better crack detection than that of simple CNN. The use of augmentation techniques increases the size of dataset which indirectly boosts the network performance. New loss functions can resolve the imbalanced data issue.
Structural crack detection using deep convolutional neural networks
Abstract Convolutional Neural Networks (CNN) have immense potential to solve a broad range of computer vision problems. It has achieved encouraging results in numerous applications of engineering, medical, and other research fields due to the advancement in hardware, data collection procedures, and efficient algorithms. These innovations have changed the way how specific problems are solved as compared to conventional methods. This article presents a review of CNN implementation on civil structure crack detection. The review highlights the significant research that has been performed to detect structure cracks through classification and segmentation of crack images with CNN in the perspective of image pre-processing techniques, processing hardware, software tools, datasets, network architectures, learning procedures, loss functions, and network performance. The key contribution of this review article is the study and analysis of the most recent developments on crack detection using CNN. Additionally, this work also presents a discussion on crack detection through a manual process, image processing techniques, and machine learning methods along with their limitations. Finally, this article aims for assisting the readers to understand the motivation and methodology of the various CNN-based crack detection methods and to invoke them for exploring the solutions of challenges outlined in future research.
Highlights The recent crack detection methods have adopted Convolutional Neural Networks for crack classification and segmentation. Manual crack inspection, IPT's and other traditional ML methods and techniques have several limitations and drawbacks. The performance of encoder-decoder architectures has better crack detection than that of simple CNN. The use of augmentation techniques increases the size of dataset which indirectly boosts the network performance. New loss functions can resolve the imbalanced data issue.
Structural crack detection using deep convolutional neural networks
Ali, Raza (Autor:in) / Chuah, Joon Huang (Autor:in) / Talip, Mohamad Sofian Abu (Autor:in) / Mokhtar, Norrima (Autor:in) / Shoaib, Muhammad Ali (Autor:in)
01.10.2021
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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