As an important part of road traffic, bridges need to be regularly inspected and maintained during their service life. Among them, the crack width of bridges is an important inspection index. Traditional crack width detection requires special vehicles to occupy fixed lanes, which affects traffic and incurs high costs. In this paper, a rotor UAV is used as the platform, and a distance sensor and relevant protective measures are added. The image information of bridge cracks is collected through close proximity photography. After image processing, correction, and calibration with professional instruments, the crack width is calculated. Through the detection of the actual crack width and comparison with the traditional detection method, it is shown that the UAV close proximity photography method can meet the identification requirements for the crack width of bridges larger than 0.2 mm, realizes low-cost detection, and has certain application prospects.
Key words
UAV /
close proximity photography /
distance sensor /
crack width
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
References
[1] Adhikari R S,Moselhi O,Bagchi A.Image-based retrieval of concrete crack properties for bridge inspection[J].Automation in Construction,2014,39(4):180-194.
[2] Lee B Y,Kim Y Y,Yi S T,et al.Automated image processing technique for detecting and analyzing concrete surface cracks[J].Structure and Infrastructure Engineering,2013,9(6):567-577.
[3] Nguyen H N,Kam T Y,Cheng P Y.A novel automatic concrete surface crack identification using isotropic undecimated wavelet transform[C]//International Symposium on Intelligent Signal Processing and Communication System.New York:Institute of Electrical and Electronics Engineers,2012.
[4] Chen J H,Su M C,Cao R.A self organizing map optimization based image recognition and processing model for bridge crack inspection[J].Automation in Construction,2016,73:58-66.
[5] Li G,He S,Ju Y,et al.Long-distance precision inspection method for bridge cracks with image processing[J].Automation in Construction,2014,41:83-95.
[6] Rose M R,Arnold R D.Unmanned aircraft systems selection practices current research and future directions[J].Military Psychology,2013,25(5):413-427.
[7] 蔡鑫. 复杂环境下无人机安全控制与协同编队研究[D].北京:军事科学院,2024.
[8] 戴启凡. 桥梁检测爬壁机器人及其自适应控制技术研究[D].南京:南京理工大学,2014.
[9] 陈时雨. 基于深度强化学习的旋翼无人机敏捷飞行控制与运动规划[D].哈尔滨:哈尔滨工业大学,2024.
[10] 占继刚. 基于图像处理的桥梁底面裂缝检测识别方法研究[D].北京:北京交通大学,2017.
[11] 陈金桥. 基于无人机图像的混凝土桥梁表观病害识别研究[D].南京:东南大学,2020.
[12] 廖祥灿. 无人机贴近摄影测量的桥梁表观病害检测方法研究[D].淄博:山东理工大学,2023.
[13] 闫振. 桥梁检测飞行爬壁机器人的设计与研究[D].重庆:重庆交通大学,2024.
[14] 杨陶. 径向可变形多旋翼无人机的设计与控制研究[D].哈尔滨:哈尔滨工业大学,2023.
[15] 王鹏. 基于无人机视频的桥梁裂缝识别方法研究[D].广州:华南理工大学,2018.
[16] 王枫,吴华勇,赵荣欣,等.基于无人机桥梁表观病害巡检与三维数字孪生[J].城市道桥与防洪,2023(10):229-232+245+28-29.
[17] 张劲泉,晋杰,汪云峰,等.公路桥梁智能检测技术与装备研究进展[J].公路交通科技,2023,40(1):1-27+58.
[18] 李洒,杨建明,巩超.面向平飞姿态的四旋翼无人机机身造型设计[J].机械设计,2025,42(2):186-192.
[19] 李虔. 多传感器融合技术在公路桥梁综合检测中的研究[J].时代汽车,2025(4):144-146.
[20] 卢颖. 基于三维激光扫描的桥梁检测技术应用研究[D].长春:吉林大学,2017.
[21] 南鹏.基于无人机影像技术的桥梁裂缝检测方法研究[J].交通世界,2024(22):164-166+174.
[22] 钟新谷,彭雄,沈明燕.基于无人飞机成像的桥梁裂缝宽度识别可行性研究[J].土木工程学报,2019,52(4):52-61.
[23] 彭雄. 基于无人飞机机器视觉的结构缺陷智能识别方法及系统[D].湘潭:湖南科技大学,2022.
[24] 李波,李晗,胡正伟.无人机贴近摄影测量技术在建筑立面测量中的应用[J].测绘通报,2024(S2):32-36.
