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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 …