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2025 | OriginalPaper | Buchkapitel

Intelligence Approach for Road Crack Detection Based on Real-World Measurement

verfasst von : Jia Meng, Weixing Hong, Abdoul Fatakhou Ba, Ahmed Silik, Mohammad Noori, Wael A. Altabey

Erschienen in: Proceedings of the 4th International Civil Engineering and Architecture Conference

Verlag: Springer Nature Singapore

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Abstract

Road crack detection and measurement is one of the most important challenges for driving safety. Although the problem has been studied for a long time, few researchers have been investigating the road crack real-world measurements. Here, we develop an intelligence approach that detects the road crack then applies image processing techniques for the segmentation and real-world measurements. Our method demonstrates to be able to detect and measure the crack real-world area. The present intelligence approach including improved architecture CNN-YOLOv5 (Convolutional Neural Network-You Only Look Once version5), YOLO-crack were proposed to improve the ability of the network to detect multiple cracks. On the one hand, YOLO-crack realizes cracks detection on images. In CNN-YOLOv5, a hybrid module is proposed, which combines CNN and YOLOv5 to extract sparse the multi-scale features expressed can avoid the loss of local information caused by dilated convolution.

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Vorheriges Kapitel Structural Member Strength Prediction Using Backpropagation Neural Network: A Tool for Retrofitting Intervention Integrating Non-linear Static Analysis

Nächstes Kapitel Research on Temperature and Humidity Cracks in Early Age of Track Slab of Double-Block Ballastless Track Structure

Silik A, Hong W, Li J, Mao M, Noori M, Altabey WA (2023) Develop a health monitoring technique for analysis a big data of bridges. Lect Notes Civil Eng 292:59–78. https://doi.org/10.1007/978-981-19-5783-3_5CrossRef

Guo D, Weixing H, Altabey WA (2023) Pavement pothole monitoring via computer vision technology. In: 2023 IEEE international smart cities conference (ISC2 2023). University POLITEHNICA of Bucharest, Romania. https://doi.org/10.1109/ISC257844.2023.10293626

Guo D, Weixing H, Altabey WA (2023) Intelligence approach for structures crack monitoring based on UAV-deep-learning approach. In: 2023 IEEE international smart cities conference (ISC2 2023). University POLITEHNICA of Bucharest, Romania. https://doi.org/10.1109/ISC257844.2023.10293349

Hong W, Li H, Xu Y, Zhang C, Li J, Noori M, Altabey WA (2023) Damage identification in bridges based on WPECD transform. In: 22nd international scientific conference engineering for rural development, ERD 2023 Jelgava, vol 22, pp 1039–1044, 191585. https://doi.org/10.22616/ERDev.2023.22.TF211

Silik A, Wang X, Mei C, Jin X, Zhou X, Zhou W, Chen C, Hong W, Li J, Mao M, Liu Y, Noori M, Altabey WA (2023) Development of features for early detection of defects and assessment of bridge decks. SDHM Struct Durability Health Monit 17(4):257–281. https://doi.org/10.32604/sdhm.2023.023617

Yanliang X, Chuanyue Z, Gongzuo S, Ye J, Yujin Q, Xueshun M, Jingtao X, Noori M, Altabey WA (2023) Research of pavement crack detection system based on image processing. In: Proceedings of SPIE 12590, third international conference on computer vision and information technology (CVIT 2022), p 1259007. https://doi.org/10.1117/12.2669715

Moghadam KY, Noori M, Silik A, Altabey WA (2024) Damage detection in structures by using imbalanced classification algorithms. Mathematics 12(3):432. https://doi.org/10.3390/math12030432

Altabey WA, Kouritem SA, Al-Moghazy MA (2024) Apply frequency response function schemes for damage detection in composite nanoscale-pipes under transient conditions. Nano-Struct Nano-Objects 39:101259. https://doi.org/10.1016/j.nanoso.2024.101259

Altabey WA (2024) A comprehensive study of a long-term creep thermo-mechanical fatigue behavior monitoring of BFRP composite pipeline using electrical capacitance sensors and deep learning algorithm. Int J Fatigue 184:108277. https://doi.org/10.1016/j.ijfatigue.2024.108277

10.

Wang T, Li H, Noori M, Ghiasi R, Kuok S, Altabey WA (2023) Seismic response prediction of structures based on Runge-Kutta recurrent neural network with prior knowledge. Eng Struct 279:115576. https://doi.org/10.1016/j.engstruct.2022.115576CrossRef

11.

Liping Z, Hong W, Altabey WA (2024) Bridges Damage assessment techniques improvement through machine learning algorithm. Lect Notes Civil Eng 336:82–92. https://doi.org/10.1007/978-981-99-5716-3_6CrossRef

12.

Dangui G, Hong W, Altabey WA (2024) Monitoring of bridges damage based on the system transfer function maps from sensors datasets. Lect Notes Civil Eng 336:71–81. https://doi.org/10.1007/978-981-99-5716-3_5CrossRef

13.

Altabey WA (2020) An exact solution for acoustic simulation based transmission loss optimization of double-chamber silencer. Sound Vibration 54(4):215–224. https://doi.org/10.32604/SV.2020.011516

14.

Altabey WA, Noori M (2024) Mathematical modeling of BFRP laminated composite double-chamber mufflers based acoustic transmission loss optimization, vol 486. Lecture Notes in Civil Engineering, Springer, Cham, Switzerland. https://doi.org/10.1007/978-3-031-57224-1_6

15.

Altabey WA, Noori M, Wu Z, Silik A, Vasilis S (2023) Enhancement of structural health monitoring framework on beams based on k-nearest neighbor algorithm. In: Proceedings of the 14th international workshop on structural health monitoring (IWSHM 2023): statistical methods and machine learning. Stanford University, California, USA, paper No. 2926. ISBN 978-1-60595-693-0

16.

Silik A, Noori M, Altabey WA, Ji D, Ghiasi R (2022) A new denoising technique via wavelet analysis of structural vibration response for structural health monitoring applications. In: Lifeline 2022: advancing lifeline engineering for community resilience, ASCE library, pp 691–706. https://doi.org/10.1061/9780784484449.063

17.

Altabey WA, Noori M, Wu Z, Al-Moghazy MA, Kouritem SA (2022) Studying acoustic behavior of BFRP laminated composite in dual-chamber muffler application using deep learning algorithm. Materials 15(22):807. https://doi.org/10.3390/ma15228071CrossRef

18.

Altabey WA, Kouritem SA, Abouheaf MI, Nahas N (2022) A deep learning-based approach for pipeline cracks monitoring. In: IEEE conference, 2nd international conference on electrical, computer, communications and mechatronics engineering (ICECCME-2022). Maldives. https://doi.org/10.1109/ICECCME55909.2022.9987998

19.

Altabey WA, Kouritem SA, Abouheaf MI, Nahas N (2022) Research in image processing for pipeline crack detection applications. In: IEEE conference, 2nd international conference on electrical, computer, communications and mechatronics engineering (ICECCME-2022). Maldives. https://doi.org/10.1109/ICECCME55909.2022.9988417

20.

Weixing H, Ju W, Dangui G, Xiaodong W, Gongzuo S, Yan-Liang X, Chuanyue Z, Noori M, Altabey WA (2022) Artificial intelligence technique for pavement diseases identification, ACM digital library. In: Proceedings of the 4th international conference on intelligent science and technology (ICIST ‘22), pp 66–72. https://doi.org/10.1145/3568923.3570515

21.

Silik A, Noori M, Ghiasi R, Wang T, Kuok S, Farhan NSD, Dang J, Wu Z, Altabey WA (2023) Dynamic wavelet neural network model for damage features extraction and patterns recognition. Civil Struct Health Monit. https://doi.org/10.1007/s13349-023-00683-8CrossRef

22.

Hong W, Noori M, Jiang H, Liu Y, Altabey WA (2023) Machine vision-based structural diagnosis application. Lect Notes Civil Eng 292:79–88. https://doi.org/10.1007/978-981-19-5783-3_6CrossRef

23.

Zhang C, Xu Y, Silik A, Li H, Noori M, Altabey WA (2023) Analysis of instantaneous frequency for structure condition tracking using time-frequency representation: comparative study. In: 22nd international scientific conference engineering for rural development, ERD 2023 Jelgava, vol 22, pp 1045–1051, 191585. https://doi.org/10.22616/ERDev.2023.22.TF212

24.

Ghiasi R, Noori M, Altabey WA, Wang T, Wu Z (2022) Structural damage detection under uncertain parameters using non-probabilistic meta-model and interval mathematics. In: Lifeline 2022: advancing lifeline engineering for community resilience, ASCE library, pp 670–679. https://doi.org/10.1061/9780784484449.061

25.

Altabey WA, Noori M, Wu Z, Silik A, Farhan NSD (2023) Effective technique for structures damage detection based on the structural frequency maps. In: 5th ECCOMAS thematic conference on uncertainty quantification in computational sciences and engineering (UNCECOMP 2023). Athens, Greece

26.

Altabey WA, Noori M, Wu Z (2023) Deep learning-based crack, location and area identification for a pipeline by the convolutional neural network based on crack contour network method. In: 5th ECCOMAS thematic conference on uncertainty quantification in computational sciences and engineering (UNCECOMP 2023). Athens, Greece

Titel: Intelligence Approach for Road Crack Detection Based on Real-World Measurement
verfasst von: Jia Meng
Weixing Hong
Abdoul Fatakhou Ba
Ahmed Silik
Mohammad Noori
Wael A. Altabey
Verlag: Springer Nature Singapore
Buch: Proceedings of the 4th International Civil Engineering and Architecture Conference
Print ISBN: 978-981-9754-76-2

Electronic ISBN: 978-981-9754-77-9

Copyright-Jahr: 2025
DOI: https://doi.org/10.1007/978-981-97-5477-9_7

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