Application Status and Prospect on Computer Vision Technology Application in Bridge and Tunnel Engineering

YANG Xingyu; CHEN Guangyao; ZHU Juntong; XU Zhao

doi:10.3724/j.gyjzG23051209

Volume 54 Issue 9

Sep. 2024

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YANG Xingyu, CHEN Guangyao, ZHU Juntong, XU Zhao. Application Status and Prospect on Computer Vision Technology Application in Bridge and Tunnel Engineering[J]. INDUSTRIAL CONSTRUCTION, 2024, 54(9): 209-218. doi: 10.3724/j.gyjzG23051209

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YANG Xingyu, CHEN Guangyao, ZHU Juntong, XU Zhao. Application Status and Prospect on Computer Vision Technology Application in Bridge and Tunnel Engineering[J]. INDUSTRIAL CONSTRUCTION, 2024, 54(9): 209-218. doi: 10.3724/j.gyjzG23051209

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Application Status and Prospect on Computer Vision Technology Application in Bridge and Tunnel Engineering

doi: 10.3724/j.gyjzG23051209

1. School of Civil Engineering, Southeast University, Nanjing 211189, China;
2. Department of Software, Southeast University, Suzhou 215000, China

Received Date: 2023-05-12
Available Online: 2024-10-18

Abstract

Abstract

Bridge and tunnel engineering is an important branch in the field of civil engineering. With the increasing digitization of construction and upgrades in hardware equipment, computer vision has become a key technology supporting the digital development of bridge and tunnel engineering. To comprehensively reveal the research hotspots and trends of computer vision in the field of bridge and tunnel engineering, the paper focused on the application of computer vision in bridge and tunnel engineering, used knowledge graph tools to conduct visualized analysis of relevant literature, and systematically summarized the theoretical and applied technologies of computer vision tasks, including image processing and feature extraction, object detection and tracking, object classification and recognition, 3D reconstruction and SLAM, and intelligent analysis and decision-making. Based on this, the paper also summarized the current research difficulties from four aspects: dataset defects, image accuracy, detection real-time performance, and algorithm applicability. The paper also identified and discussed solutions to application difficulties and provided prospects for future development, so as to provide theoretical support for further research and technological application.
- computer vision,
- bridge and tunnel construction,
- project management,
- information technology,
- construction technology

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References(48)

References

[1]	PANERU S, JEELANI I. Computer vision applications in construction: current state, opportunities & challenges[J]. Automation in Construction, 2021, 132, 103940.
[2]	SASAMA H, UKAI M, OHTA M, et al. Inspection system for railway facilities using continuously scanned image[J].IEEJ Transactions on Electronics, Information and Systems, 1997, 117(10): 1345-1354.
[3]	GORDON S, LICHTI D, STEWART M. Application of a high-resolution, ground-based laser scanner for deformation measurements[C]//Proceedings of 10th International FIG Symposium on Deformation Measurements. Orange: [s.n.]. 2001: 23-32.
[4]	YOON J S, SAGONG M, LEE J S, et al. Feature extraction of a concrete tunnel liner from 3D laser scanning data[J]. NDT & E International, 2009, 42(2):97-105.
[5]	KRIZHEVSKY A, SUTSKEVER I, HINTON G E. Imagenet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90.
[6]	CHA Y J, CHOI W, BÜYÜKÖZTÜRK O. Deep learning-based crack damage detection using convolutional neural networks[J]. Computer-Aided Civil and Infrastructure Engineering, 2017, 32(5): 361-378.
[7]	HAN K, WANG Y, CHEN H, et al. A survey on vision transformer[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 45(1): 87-110.
[8]	XIA Y, NIE B, ZHANG Y, et al. Design and implementation of tunnel image mosaic system based on open CV[J]. International Journal of System Assurance Engineering and Management, 2020, 11(4): 792-797.
[9]	WU R, FUJITA Y, SOGA K. Integrating domain knowledge with deep learning models: an interpretable AI system for automatic work progress identification of NATM tunnels[J]. Tunnelling and Underground Space Technology, 2020, 105, 103558.
[10]	ZHANG L, YANG F, ZHANG Y, et al. Road crack detection using deep convolutional neural network[C]//Proceedings of the 2016 IEEE International Confere-nce on Image Processing. Washington:IEEE Computer Society, 2016: 3708-3712.
[11]	朱苏雅, 杜建超, 李云松,等. 采用U-Net卷积网络的桥梁裂缝检测方法[J]. 西安电子科技大学学报, 2019, 46(4): 35-42.
[12]	YAN X, ZHOU G, ZHAO X. Method for rapid detection and treatment of cracks in tunnel lining based on deep learning[C]//Health Monitoring of Structural and Biological Systems. Los Angeles, California: 2020.
[13]	DOULAMIS A, DOULAMIS N, PROTOPAPADAKIS E, et al. Combined convolutional neural networks and fuzzy spectral clustering for real time crack detection in tunnels[C]//2018 25th IEEE International Conference on Image Processing (ICIP). Athens: 2018: 4153-4157.
[14]	YANG P F, WANG C. Research of bridge crack detecting system based on machine vision[J]. Advanced Materials Research, 2012,466-467: 1197-1201.
[15]	NGUYEN C K, KAWAMURA K, SHIOZAKI M, et al. Development of an automatic crack inspection system for concrete tunnel lining based on computer vision technologies[C]//International Conference on Materials and Construction. Nha Trang, Vietnam: 2018.
[16]	阮小丽, 王波, 吴巨峰,等. 基于深度学习的钢筋混凝土桥梁掉块露筋病害识别[J]. 世界桥梁, 2020, 48(6): 88-92.
[17]	JO B W, LEE Y S, JO J H, et al. Computer vision-based bridge displacement measurements using rotation-invariant image processing technique[J]. Sustainability, 2018, 10(6), 1785.
[18]	崔弥达, 王超, 陈金桥,等. 基于ROS及YOLOv3的混凝土桥梁裂缝实时检测系统[J]. 东南大学学报(自然科学版), 2023, 53(1): 61-66.
[19]	FAN S, ZHOU Q. Multi-agent system for tunnel-settlement monitoring: a case study in Shanghai[J]. Displays, 2021,69,102041.
[20]	孔烜, 李思琪, 韩振勇,等. 适用于中小跨径桥梁频率识别的移动检测车辆参数研究[J]. 湖南大学学报(自然科学版), 2023, 50(7): 12-22.
[21]	XU R, YE H, HU B, et al. Intelligent dimming control and energy consumption monitoring system of tunnel lighting[J]. Lighting Research & Technology, 2024, 56(1): 72-86.
[22]	黄丰, 莫辉强, 王伟,等. 一种基于深度学习的视频客流密度计算方法[J]. 计算机与数字工程, 2022, 50(10): 2149-2152 , 2165.
[23]	CHEN J, DENG S, WANG P, et al. Lightweight helmet detection algorithm using an improved YOLOv4[J]. Sensors, 2023, 23(3), 1256.
[24]	PRASANNA P, DANA K, GUCUNSKI N, et al. Computer-vision based crack detection and analysis[C]//Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2012. Piscataway: 2012: 1143-1148.
[25]	LIANG X. Image-based post-disaster inspection of reinforced concrete bridge systems using deep learning with Bayesian optimization[J]. Computer-Aided Civil and Infrastructure Engineering, 2019, 34(5): 415-430.
[26]	ZHU J, ZHANG C, QI H, et al. Vision-based defects detection for bridges using transfer learning and convolutional neural networks[J]. Structure and Infrastructure Engineering, 2020, 16(7): 1037-1049.
[27]	WANG H, WANG Q, ZHAI J, et al. Design of Fast Acquisition System and Analysis of Geometric Feature for Highway Tunnel Lining Cracks Based on Machine Vision[J]. Applied Sciences, 2022, 12(5), 2516.
[28]	YU J C, YI T H, ZHANG S H, et al. Automatic quantitative identification of bridge surface cracks based on deep learning[J]. Journal of Performance of Constructed Facilities, 2023, 37(1). DOI: 10.1061/JPCFEV.CFENG-4238.
[29]	KHUC T, CATBAS F N. Computer vision-based displacement and vibration monitoring without using physical target on structures[J]. Structure and Infrastructure Engineering, 2017, 13(4): 505-516.
[30]	DING L, FANG W, LUO H, et al. A deep hybrid learning model to detect unsafe behavior: integrating convolution neural networks and long short-term memory[J]. Automation in Construction, 2018,86: 118-124.
[31]	段品生, 周建亮. 基于姿态特征的建筑工人不安全行为刻画方法[J]. 安全与环境工程, 2022,29(3): 1-8.
[32]	张宇, 阳军生, 祝志恒,等. 基于图像点云的多维度隧道初期支护大变形监测研究和应用[J]. 隧道建设(中英文), 2021, 41(5): 795-802.
[33]	MIRZAZADE A, POPESCU C, GONZALEZ-LIBREROS J, et al. Semi-autonomous inspection for concrete structures using digital models and a hybrid approach based on deep learning and photogrammetry[J]. Journal of Civil Structural Health Monitoring, 2023,13(8): 1633-1652.
[34]	WANG X, DEMARTINO C, NARAZAKI Y, et al. Rapid seismic risk assessment of bridges using UAV aerial photogrammetry[J]. Engineering Structures, 2023, 279,115589.
[35]	ZHU J, LI X, ZHANG C, et al. An accurate approach for obtaining spatiotemporal information of vehicle loads on bridges based on 3D bounding box reconstruction with computer vision[J]. Measurement, 2021, 181,109657.
[36]	LEUNG K, LÜHR D, HOUSHIAR H, et al. Chilean underground mine dataset[J]. The International Journal of Robotics Research, 2017, 36(1): 16-23.
[37]	张明媛, 曹志颖, 赵雪峰,等. 基于深度学习的建筑工人安全帽佩戴识别研究[J]. 安全与环境学报, 2019, 19(2): 535-541.
[38]	FANG W, DING L, LUO H, et al. Falls from heights: a computer vision-based approach for safety harness detection[J]. Automation in Construction, 2018, 91(7):53-61.
[39]	GUO H, ZHANG Z, YU R, et al. Action recognition based on 3D skeleton and LSTM for the monitoring of construction workers’ safety harness usage[J]. Journal of Construction Engineering and Management, 2023, 149(4). DOI: 10.1061/JCEMD4.COENG-12542.
[40]	FANG W, MA L, LOVE P E D, et al. Knowledge graph for identifying hazards on construction sites: Integrating computer vision with ontology[J]. Automation in Construction, 2020, 119, 103310.
[41]	XU W, WANG T K. Construction worker safety prediction and active warning based on computer vision and the gray absolute decision analysis method[J]. Journal of Construction Engineering and Management, 2023, 149(4). DOI: 10.1061/JCEMD4.COENG-12695.
[42]	常丽, 张雪, 蒋辉,等. 融合YOLOv5s与SRGAN的实时隧道火灾检测[J]. 电子测量与仪器学报, 2022, 36(8): 223-230.
[43]	张金雷, 杨立兴, 陈瑶,等. 基于计算机视觉的轨道交通站内客流识别与预测方法:CN114612860A[P]. 2022-06-10.
[44]	杨祖莨, 丁洁, 刘晋峰. 一种新的结合卷积神经网络的隧道内停车检测方法[J]. 重庆大学学报, 2021, 44(6): 49-59.
[45]	AHMED M, MASOOD S, AHMAD M, et al. Intelligent driver drowsiness detection for traffic safety based on multi CNN deep model and facial subsampling[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 23(10): 19743-19752.
[46]	罗锦钊,孙玉龙,钱增志,等.人工智能大模型综述及展望[J].无线电工程, 2023,53(11): 2461-2472.
[47]	覃思中,郑哲,顾燚,等.大语言模型在建筑工程中的应用测试与讨论[J].工业建筑, 2023,53(9): 162-169.
[48]	OSCO L P, LEMOS E L, GONÇALVES W N, et al. The potential of visual ChatGPT for remote sensing[J]. Remote Sensing, 2023, 15(13), 3232.