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.

卷积神经网络 (CNN) 具有解决各种计算机视觉问题的巨大潜力。由于硬件、数据收集程序和高效算法的进步，它在工程、医学和其他研究领域的众多应用中取得了令人鼓舞的成果。与传统方法相比，这些创新改变了解决特定问题的方式。本文回顾了 CNN 在土木结构裂缝检测方面的实现。该评论强调了从图像预处理技术、处理硬件、软件工具、数据集、网络架构、学习程序、损失函数、和网络性能。这篇评论文章的主要贡献是研究和分析了使用 CNN 进行裂纹检测的最新进展。此外，这项工作还讨论了通过手动过程进行裂纹检测、图像处理技术和机器学习方法及其局限性。最后，本文旨在帮助读者理解各种基于 CNN 的裂纹检测方法的动机和方法，并调用它们来探索未来研究中概述的挑战的解决方案。和机器学习方法及其局限性。最后，本文旨在帮助读者理解各种基于 CNN 的裂纹检测方法的动机和方法，并调用它们来探索未来研究中概述的挑战的解决方案。和机器学习方法及其局限性。最后，本文旨在帮助读者理解各种基于 CNN 的裂纹检测方法的动机和方法，并调用它们来探索未来研究中概述的挑战的解决方案。