Article references

Articles citing this article

The Citing articles tool gives a list of articles citing the current article.
The citing articles come from EDP Sciences database, as well as other publishers participating in CrossRef Cited-by Linking Program. You can set up your personal account to receive an email alert each time this article is cited by a new article (see the menu on the right-hand side of the abstract page).

Cited article:

YOLO V5s-based Deep Learning Approach for Concrete Cracks Detection

Zhen Yu

SHS Web Conf., 144 (2022) 03015

Published online: 2022-08-26

DOI: https://doi.org/10.1051/shsconf/202214403015

This article has been cited by the following article(s):

Xiaoqin Yang, Syazwina Alias and Hui Wang
766 (2025)
https://doi.org/10.1109/ICMLAS64557.2025.10968194

Damage characteristics and YOLO automated crack detection of fissured rock masses under true-triaxial mining unloading conditions

Wenpu Li, Zhao Gao, Guorui Feng, Ruiqing Hao, Yuguo Zhou, Yaoguang Chen, Shilong Liu, Huan Zhang and Tao Wang
Engineering Fracture Mechanics 314 110790 (2025)
https://doi.org/10.1016/j.engfracmech.2024.110790

Superficial Defect Detection for Concrete Bridges Using YOLOv8 with Attention Mechanism and Deformation Convolution

Tijun Li, Gang Liu and Shuaishuai Tan
Applied Sciences 14 (13) 5497 (2024)
https://doi.org/10.3390/app14135497

Application of Mask R-CNN and YOLOv8 Algorithms for Concrete Crack Detection

Yongjin Choi, Byongkyu Bae, Taek Hee Han and Jaehun Ahn
IEEE Access 12 165314 (2024)
https://doi.org/10.1109/ACCESS.2024.3469951

A novel YOLOv8-GAM-Wise-IoU model for automated detection of bridge surface cracks

Chenqin Xiong, Tarek Zayed and Eslam Mohammed Abdelkader
Construction and Building Materials 414 135025 (2024)
https://doi.org/10.1016/j.conbuildmat.2024.135025

Crack detection in buildings using the YOLO v8 network

Weiglas Soriano Ribeiro, Juliette Zanetti, Lucas Broseghini Totola, Sérgio Ândrigo Colaço Junqueira and Pedro Henrique Pina Lauff
Revista ALCONPAT 14 (3) (2024)
https://doi.org/10.21041/ra.v14i3.765

AI-based technology to prognose and diagnose complex crack characteristics of railway concrete sleepers

Sakdirat Kaewunruen, Abdullah Abimbola Adesope, Junhui Huang, Ruilin You and Dan Li
Discover Applied Sciences 6 (5) (2024)
https://doi.org/10.1007/s42452-024-05880-8

Research on the Efficiency of Bridge Crack Detection by Coupling Deep Learning Frameworks with Convolutional Neural Networks

Kaifeng Ma, Xiang Meng, Mengshu Hao, Guiping Huang, Qingfeng Hu and Peipei He
Sensors 23 (16) 7272 (2023)
https://doi.org/10.3390/s23167272

Vision-based concrete crack detection using deep learning-based models

Elham Nabizadeh and Anant Parghi
Asian Journal of Civil Engineering 24 (7) 2389 (2023)
https://doi.org/10.1007/s42107-023-00648-8