Research on Detection of Concrete Surface Cracks by Image Processing Based on Smartphone APP
NI Tong-yuan1,2,ZHOU Ruo-xu1,YANG yang1,2,YANG Xue-cheng3,ZHANG Wu-yi4,LIN Chu-xuan1
1. College of Civil Engineering,Zhejiang University of Technology, Hangzhou,Zhejiang 310023,China
2. Key Laboratory of Civil Engineering Structures & Disaster Prevention and Mitigation Technology of Zhejiang Province,Hangzhou,Zhejiang 310023,China
3. Taizhou Yuanhe Construction Development Co Ltd., Taizhou,Zhejiang 318000,China
4. Zhejiang Traffic Detection Co Ltd., Hangzhou,Zhejiang 311215,China
Abstract:A nondestructive detection method for the concrete surface crack based on Android APP is proposed, to collect crack area image by using smartphone camera module, and to extract target crack through image processing algorithms such as graying, filtering and denoising, flood filling, etc. The crack length, width and area characteristic value are measured in order to realize the crack nondestructive testing. It is based on the geometric relationship between crack pixel coordinates and the number of pixel points contained in the crack, and combines the unit pixel size of phones camera. The newly developed APP of crack detection technology can measure the width of any point, total crack length, crack area and maximum crack width. The experimental results show that it can effectively improve the accuracy of APP to measure and detect of concrete surface cracks by using smart phone optical zoom to enlarge the target cracks. The accuracy error of APP measurement is less than 5% compared with the crack width meter, and it meets the engineering application requirements.
倪彤元,周若虚,杨杨,杨学成,张武毅,林楚轩. 基于智能手机APP的图像法检测混凝土表面裂缝研究[J]. 计量学报, 2021, 42(2): 163-170.
NI Tong-yuan,ZHOU Ruo-xu,YANG yang,YANG Xue-cheng,ZHANG Wu-yi,LIN Chu-xuan. Research on Detection of Concrete Surface Cracks by Image Processing Based on Smartphone APP. Acta Metrologica Sinica, 2021, 42(2): 163-170.
[1]鞠丽艳. 混凝土裂缝抑制措施的研究进展[J]. 混凝土, 2002, 151(5): 11-14.
Ju L Y. Research progress of concrete crack suppression measures[J]. Concrete, 2002, 151(5): 11-14.
[2]刘科, 侯立新, 卞昕. 基于机器视觉的仪表示值识别算法研究[J]. 计量学报, 2013, 34(5): 425-429.
Liu K, Hou L X, Bian X. Study on Digit Recognition for Digital Meter Based on Machine Vision[J]. Acta Metrologica Sinica, 2013, 34(5): 425-429.
[3]张沫, 郑慧峰, 朱勤丰. 基于图像处理的声相云图评价方法研究[J]. 计量学报, 2020, 41(8): 983-988.
Zhang M, Zheng H F, Zhu Q F. Research on Evaluation Method of Acoustic Phase Image Based on Image Processing[J].Acta Metrologica Sinica, 2020, 41(8): 983-988.
[4]倪彤元, 张武毅, 杨杨, 等. 基于图像处理的桥梁混凝土裂缝检测研究进展[J]. 城市道桥与防洪, 2019, 243(7): 258-263, 29-30.
Ni T Y, Zhang W Y, Yang Y, et al. Research Progress of Bridge Fracture Detection Technology Based on Image Processing Technology[J]. Urban Roads Bridges & Flood Control, 2019, 243(07): 258-263, 29-30.
[5]许薛军. 基于数字图像的道路工程构造物开裂与表面变形识别技术[D]. 广州: 华南理工大学, 2014.
[6]Mohan A, S, Poobal S. Crack Detection Using Image Processing: A Critical Review and Analysis [J]. Alexandria Engineering Journal, 2018, 57(2): 787-798.
[7]Yamaguchi T, Nakamura S, Saegusa R, et al. Image-Based Crack Detection for Real Concrete Surfaces[J]. IEEJ Transactions on Electrical and Electronic Engineering, 2008, 3(1): 128-135.
[8]Smarzewski P. Processes of Cracking and Crushing in Hybrid Fibre Reinforced High-Performance Concrete Slabs [J]. Processes, 2019, 7(1): 49-71.
[9]高一凡, 蔡静, 张学聪, 等. 基于NETD的红外热像仪图像预处理方法研究[J]. 计量学报, 2019, 40(6): 1020-1024.
Gao Y F, Cai J, Zhang X C, et al. Research on Image Preprocessing Method of Infrared Camera Based on NETD[J]. Acta Metrologica Sinica, 2019, 40(6): 1020-1024.
[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]景凯. 基于智能手机的混凝土结构裂缝检测软件的开发研究[D]. 杭州: 浙江工业大学, 2016.
[12]那立阳. 基于Android的路面破损检测软件设计与实现[D]. 齐齐哈尔: 齐齐哈尔大学, 2016.
[13]Ni T Y, Zhou R X, Gu C P, et al. Measurement of Concrete Crack Feature with Android Smartphone APP Based on Digital Image Processing Techniques[J]. Measurement, 2020, 150(1): 1-8.
[14]倪彤元, 杨杨, 周若虚, 等. 基于android系统的混凝土表面裂缝特征的APP检测方法:中国,CN110276752A[P]. 2019-09-24.
[15]易正明, 鄢明, 迟云广, 等. 基于图像处理的回转窑火焰温度测量技术研究[J]. 计量学报, 2008, 29(1): 42-45.
Yi Z M, Yan M, Chi Y G, et al. Measurement Technique of Flame Temperature in Rotary Kiln Based on Image Processing[J]. Acta Metrologica Sinica, 2008, 29(1): 42-45.
[16]周传林. 图像处理技术在混凝土桥梁裂缝检测中的应用研究[J]. 筑路机械与施工机械化, 2014, 31(2): 74-77, 80.
Zhou C L. Research on Application of Image Processing Technology in Concrete Bridge Crack Detection[J]. Road Machinery & Construction Mechanization, 2014, 31(2): 74-77, 80.
[17]陈利华, 董志学. 基于Android的裂缝宽度检测系统设计实现[J]. 计算机工程与设计, 2013, 34(9): 3195-3199.
Chen L H, Dong Z X. Design and Implementation of Crack Width Detection System Based on Android[J]. Computer Engineering and Design, 2013, 34(9): 3195-3199.
[18]郭全民, 刘才臻. 路面病害巡检评估系统中的裂缝检测技术[J]. 国外电子测量技术, 2015, 34(7): 47-50.
Guo G M, Liu C Z. Pavement Crack Detection Method in Concrete Pavement Disease Detection and Estimation System[J]. Foreign Electronic Measurement Technology, 2015, 34(7): 47-50.
[19]李文波, 杨保春. 基于图像处理技术的混凝土桥梁裂缝宽度检测[J]. 湖南交通科技, 2015, 41(1): 119-122.
Li W B, Yang B C. Detection of Crack Width of Concrete Bridge Based on Image Processing Technology[J]. Hunan Communication Science and Technology, 2015, 4101): 119-122.
[20]邵伟,彭鹏,周阿维, 等. 工件表面低对比度缺陷快速准确识别方法[J]. 计量学报, 2019, 40(5): 793-797.
Shao W, Peng P, Zhou A W, et al. Fast and Accurate Recognition of Low-contrast Defects Workpiece Surface[J]. Acta Metrologica Sinica, 2019, 40(5): 793-797.
[21]王润. 图像边缘检测算子的适用场景研究[J]. 电脑知识与技术, 2019, 15(13): 211-214.
Wang R. Research on Applicable Scene of Image Edge Detection Operator[J]. Computer Knowledge and Technology, 2019, 15(13): 211-214.