Computer Vision-Based Detection for Delayed Fracture of Bolts in Steel Bridges

The delayed fracture of high-strength bolts occurs frequently in the bolt connections of long-span steel bridges. This phenomenon can threaten the safety of structures and even lead to serious accidents in certain cases. However, the manual inspection commonly used in engineering to detect the fract...

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Autores principales:	Jing Zhou, Linsheng Huo
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Technology (General) T1-995
Acceso en línea:	https://doaj.org/article/1a735af3a505446c95c0d7b58b929d77
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spelling	oai:doaj.org-article:1a735af3a505446c95c0d7b58b929d772021-11-22T01:10:41ZComputer Vision-Based Detection for Delayed Fracture of Bolts in Steel Bridges1687-726810.1155/2021/8325398https://doaj.org/article/1a735af3a505446c95c0d7b58b929d772021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/8325398https://doaj.org/toc/1687-7268The delayed fracture of high-strength bolts occurs frequently in the bolt connections of long-span steel bridges. This phenomenon can threaten the safety of structures and even lead to serious accidents in certain cases. However, the manual inspection commonly used in engineering to detect the fractured bolts is time-consuming and inconvenient. Therefore, a computer vision-based inspection approach is proposed in this paper to rapidly and automatically detect the fractured bolts. The proposed approach is realized by a convolutional neural network- (CNN-) based deep learning algorithm, the third version of You Only Look Once (YOLOv3). A challenge for the detector training using YOLOv3 is that only limited amounts of images of the fractured bolts are available in practice. To address this challenge, five data augmentation methods are introduced to produce more labeled images, including brightness transformation, Gaussian blur, flipping, perspective transformation, and scaling. Six YOLOv3 neural networks are trained using six different augmented training sets, and then, the performance of each detector is tested on the same testing set to compare the effectiveness of different augmentation methods. The highest average precision (AP) of the trained detectors is 89.14% when the intersection over union (IOU) threshold is set to 0.5. The practicality and robustness of the proposed method are further demonstrated on images that were never used in the training and testing of the detector. The results demonstrate that the proposed method can quickly and automatically detect the delayed fracture of high-strength bolts.Jing ZhouLinsheng HuoHindawi LimitedarticleTechnology (General)T1-995ENJournal of Sensors, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology (General) T1-995
spellingShingle	Technology (General) T1-995 Jing Zhou Linsheng Huo Computer Vision-Based Detection for Delayed Fracture of Bolts in Steel Bridges
description	The delayed fracture of high-strength bolts occurs frequently in the bolt connections of long-span steel bridges. This phenomenon can threaten the safety of structures and even lead to serious accidents in certain cases. However, the manual inspection commonly used in engineering to detect the fractured bolts is time-consuming and inconvenient. Therefore, a computer vision-based inspection approach is proposed in this paper to rapidly and automatically detect the fractured bolts. The proposed approach is realized by a convolutional neural network- (CNN-) based deep learning algorithm, the third version of You Only Look Once (YOLOv3). A challenge for the detector training using YOLOv3 is that only limited amounts of images of the fractured bolts are available in practice. To address this challenge, five data augmentation methods are introduced to produce more labeled images, including brightness transformation, Gaussian blur, flipping, perspective transformation, and scaling. Six YOLOv3 neural networks are trained using six different augmented training sets, and then, the performance of each detector is tested on the same testing set to compare the effectiveness of different augmentation methods. The highest average precision (AP) of the trained detectors is 89.14% when the intersection over union (IOU) threshold is set to 0.5. The practicality and robustness of the proposed method are further demonstrated on images that were never used in the training and testing of the detector. The results demonstrate that the proposed method can quickly and automatically detect the delayed fracture of high-strength bolts.
format	article
author	Jing Zhou Linsheng Huo
author_facet	Jing Zhou Linsheng Huo
author_sort	Jing Zhou
title	Computer Vision-Based Detection for Delayed Fracture of Bolts in Steel Bridges
title_short	Computer Vision-Based Detection for Delayed Fracture of Bolts in Steel Bridges
title_full	Computer Vision-Based Detection for Delayed Fracture of Bolts in Steel Bridges
title_fullStr	Computer Vision-Based Detection for Delayed Fracture of Bolts in Steel Bridges
title_full_unstemmed	Computer Vision-Based Detection for Delayed Fracture of Bolts in Steel Bridges
title_sort	computer vision-based detection for delayed fracture of bolts in steel bridges
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/1a735af3a505446c95c0d7b58b929d77
work_keys_str_mv	AT jingzhou computervisionbaseddetectionfordelayedfractureofboltsinsteelbridges AT linshenghuo computervisionbaseddetectionfordelayedfractureofboltsinsteelbridges
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Computer Vision-Based Detection for Delayed Fracture of Bolts in Steel Bridges

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