Aug 2026· Asian Journal of Civil Engineering· Vol 27, pp. 4745 - 4783· 0 citations· 98 references
TL;DR
A comprehensive analysis of image processing and deep learning-based crack detection techniques for civil engineering structures, including edge detection, thresholding, morphological operations, Convolutional Neural Networks (CNNs), Fully Convolutional Networks (FCNs), U-shaped convolutional networks (U-Net), You Only Look Once (YOLO), and Digital Image Correlation (DIC).
A focused review of recent advancements in vision-based concrete crack detection and measurement is presented, synthesizing findings from 27 representative and influential studies selected through an expert-driven, non-exhaustive screening process rather than a fully systematic protocol.
Surface cracks are the main signs of structural damage in pavement infrastructure networks, especially in transportation systems around the world, where it is essential to perform timely maintenance. Traditional crack detection techniques suffer from low detection accuracy, environmental sensitivity, and assessment inc...
M. Idris, Muhammad Aqif Isyraf Ali, Muhammad Khusairi Osman et al.· 2026 IEEE International Conf...· 0 citations
Cracks that develop over time in bridges, which are an important part of transportation infrastructure, pose a structural safety risk when detected late and can lead to increased repair costs. This study aims to demonstrate the effectiveness of convolutional neural network (CNN) based deep learning architectures enhanc...
M. Uçan, Ibrahim Baran Karasin· WSEAS Transactions on Signal...· 0 citations
A deep learning-based crack detection model using a hybrid U-Net architecture enhanced with a pre-trained ResNet50 encoder, Atrous Spatial Pyramid Pooling (ASPP), and attention gates is introduced, showing superior performance in accuracy, Dice coefficient, IoU, precision, and recall compared to traditional CNNs.
Hemraj Parate· Canadian journal of civil en...· 0 citations
Early and accurate detection of cracks in structures is crucial for planning structural health and maintenance processes. This study aims to automatically detect cracks on wall and bridge surfaces using deep learning methods with smartphone images. The wall and bridge datasets consist of "cracked" and "crack-free" clas...
This automated approach can aid early detection of corrosion, enhance maintenance prioritisation and reduce inspection costs for steel structures, using a database of 9920 images captured with regular, portable cell phone cameras.
Afaq Ahmad, K. Tsavdaridis· Proceedings of the Instituti...· 0 citations
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