首页|基于数字图像技术的桥梁裂缝检测综述

基于数字图像技术的桥梁裂缝检测综述

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传统的桥梁裂缝检测主要基于人眼识别,检测效率、精度低,而且人眼识别存在受光照影响大、桥塔、高墩等高空位置无法检测及主观性强的问题.近年来,国内外诸多学者为了解决上述问题,研发了许多基于数字图像技术的桥梁裂缝检测设备,像搭载高清相机的桥梁检测车、无人机、爬墩机器人等.同时,拥有高效、高精度的裂缝检测算法更是裂缝检测的基础,如何权衡检测速度与精度一直也是众多学者研究的热点问题之一.本文就近年来国内外基于数字图像技术的桥梁裂缝检测设备、相机的搭载平台与标定方法、预处理算法、传统检测算法、深度学习算法、裂缝特征计算、图像拼接算法以及裂缝的三维输出与监测等方面展开综合评述.此外,对研究过程中存在的不足进行了总结,并从桥梁裂缝检测方法、裂缝三维表达、裂缝的监测跟踪管理和桥梁刚度损失评价及预警等方面进行了展望.
Review of bridge crack detection based on digital image technology
As one of the important contents of bridge health detection,crack detection reflects the stress and damage state of bridge structure.The traditional bridge crack detection is mainly based on human eye recognition,of which efficiency and accuracy are both low.Moreover,the human eye recognition has the following problems such as effected greatly by illumination,incapability to detect in some high-altitude positions like bridge towers and high piers and strong subjectivity.In recent years,many scholars at home and abroad have developed many bridges crack detection equipment based on digital image technology to solve the above problems,such as bridge detection vehicles equipped with high-definition cameras,drones,and climbing robots.Meanwhile,the efficient and high-precision crack detection algorithm is the basis of crack detection.How to balance the detection speed and accuracy has always been one of the hot issues studied by many scholars.In this paper,the bridge crack detection equipment based on digital image technology,the platform and calibration method of camera,preprocessing algorithm,traditional detection algorithm,deep learning algorithm,crack feature calculation,image stitching algorithm and three-dimensional output and monitoring of cracks are reviewed.In addition,summaries to deficiencies in the study and prospects the bridge crack detection method,crack three-dimensional expression,crack monitoring and management,bridge stiffness loss evaluation and early warning for the future development trend.

bridge engineeringcrack identificationimage processingconvolutional neural networkdeep learningintelligent detection

杨国俊、齐亚辉、石秀名

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兰州理工大学 土木工程学院,兰州 730050

招商局重庆交通科研设计院有限公司 桥梁工程结构动力学国家重点实验室,重庆 400067

桥梁工程 裂缝识别 图像处理 卷积神经网络 深度学习 智能检测

甘肃省科技计划项目国家自然科学基金项目国家自然科学基金项目中国博士后科学基金项目

22JR5RA25051808274521680422019M653897XB

2024

吉林大学学报(工学版)
吉林大学

吉林大学学报(工学版)

CSTPCD北大核心
影响因子:0.792
ISSN:1671-5497
年,卷(期):2024.54(2)
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