结构森林边缘检测与渗流模型相结合的混凝土表面裂缝检测

doi:10.11896／j.issn.1002-137X.2018.11.046

计算机科学 ›› 2018, Vol. 45 ›› Issue (11): 288-291.doi: 10.11896／j.issn.1002-137X.2018.11.046

结构森林边缘检测与渗流模型相结合的混凝土表面裂缝检测

瞿中, 鞠芳蓉, 陈思琪

(重庆邮电大学软件工程学院重庆400065)

收稿日期:2017-10-31 发布日期:2019-02-25
作者简介:瞿中(1972-),男,博士,教授,CCF会员,主要研究方向为数字图像处理、云计算和物联网技术等,E-mail:quzhong@cqupt.edu.cn;鞠芳蓉(1993-),女,硕士生,主要研究方向为数字图像处理,E-mail:jufangr@163.com(通信作者);陈思琪(1993-),女,硕士生,主要研究方向为数字图像处理。
基金资助:
本文受重庆市基础科学与前沿技术研究项目(cstc2015jcyjBX0090,cstc2014jcyjA40033,cstc2015jcyjA40034,cstc2014jcyjA10051)资助。

Concrete Surface Cracks Detection Combining Structured Forest Edge Detection and Percolation Model

QU Zhong, JU Fang-rong, CHEN Si-qi

(School of Software Engineering,Chongqing University of Posts & Telecommunications,Chongqing 400065,China)

Received:2017-10-31 Published:2019-02-25

摘要/Abstract

摘要： 针对现有混凝土表面裂缝检测方法对不同环境下采集的裂缝图像集检测效果鲁棒性不强的问题,引入基于结构森林的学习框架来提取裂缝边缘,并融合改进的快速渗流算法检测裂缝,以保证检测精确率和效率。使用分段函数对彩色图像进行线性变换以增强裂缝,根据包含裂缝块的局部结构特征及彩色图像积分通道特征,利用结构森林边缘检测器快速提取裂缝边缘,同时结合改进的渗流模型快速渗流边缘并去噪。最后,利用形态学方法,连接较小断裂并填充孔洞。在收集的各类裂缝图像集上的实验结果表明,该算法处理速度快、鲁棒性好,且裂缝提取的精确度优于现有算法。

关键词: 边缘检测, 结构森林, 去噪, 渗流模型

Abstract: To improve the robustness of crack detection methods for different concrete surface crack images,this paper utilized structured forest based learning framework to extract crack edge,and merged improved fast percolation algorithm to detect crack,ensuring the precision and efficiency of detection.This approach enhances the crack images by using a linear transform piecewise function to conduct linear transformation for color images.Then,according to the local structured information of crack block and the integral channel features obtained from the crack edge images,the structured forest edge detector is used to extract the crack edge fast,and the improved percolation model is fused to percolate edge fast and denoise.Finally,the morphological method is used to connect small fractures and fill the holes.Experimental results on various crack image datasets show that the proposed approach is fast and robust,and it’s superior to state-of-the-art algorithms in terms of the accuracy of crack detection.

Key words: De-noising, Edge detection, Percolation model, Structured forest

中图分类号:

TP391.41

瞿中, 鞠芳蓉, 陈思琪. 结构森林边缘检测与渗流模型相结合的混凝土表面裂缝检测[J]. 计算机科学, 2018, 45(11): 288-291. https://doi.org/10.11896／j.issn.1002-137X.2018.11.046

QU Zhong, JU Fang-rong, CHEN Si-qi. Concrete Surface Cracks Detection Combining Structured Forest Edge Detection and Percolation Model[J]. Computer Science, 2018, 45(11): 288-291. https://doi.org/10.11896／j.issn.1002-137X.2018.11.046

参考文献

[1]CHAMBON S,MOLIARD J M.Automatic Road Pavement Assessment with Image Processing:Review and Comparison[OL].http://www.researchgate.net/publication/258381376.
[2]ZOU Q,LI Q Q,MAO Q Z,et al.Target-points MST for pavement crack detection [J].Geomatics and Information Science of Wuhan University,2011,36(1):71-75.(in Chinese)
邹勤,李清泉,毛庆洲,等.利用目标点最小生成树的路面裂缝检测[J].武汉大学学报(信息科学版),2011,36(1):71-75.
[3]AMHAZ R,CHAMBON S,IDIER J,et al.Automatic Crack Detection on Two-Dimensional Pavement Images:An Algorithm Based on Minimal Path Selection [J].IEEE Transactions on Intelligent Transportation Systems,2016,17(10):2718-2729.
[4]YAMAGUCHI T,NAKAMURA S,SAEGUSA R,et al.Image-based crack detection for real concrete surfaces [J].IEEJ Tran-sactions on Electrical and Electronic Engineering,2008,3(1):128-135.
[5]YAMAGUCHI T,HASHIMOTO S.Fast crack detection method for large-size concrete surface images using percolation-based image processing [J].Machine Vision and Applications,2010,21(5):797-809.
[6]XU W,TANG Z M,XU D,et al.Integrating multi-features fusion and gestalt principles for pavement crack detection [J].Journal of Computer-Aided Design & Computer Graphics,2015,27(1):147-156.(in Chinese)
徐威,唐振民,徐丹,等.融合多特征与格式塔理论的路面裂缝检测[J].计算机辅助设计与图形学学报,2015,27(1):147-156.
[7]SHI Y,CUI L M,QI Z Q,et al.Automatic Road Crack Detection Using Random Structured Forests [J].IEEE Transactions on Intelligent Transportation System,2016,17(12):3434-3445.
[8]DOLLÁR P,ZITNICK C L.Fast Edge Detection Using Structured Forests [J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2015,37(8):1558-1570.
[9]DOLLÁR P,TU Z,PERONA P,et al.Integral Channel Features[C]∥British Machine Vision Conference.London:British Machine Vision Association,2009:1-11.
[10]QU Z,LIN L,GUO Y,et al.An improved algorithm for image crack detection based on percolation model [J].IEEJ Transactions on Electrical and Electronic Engineering,2015,10(2):214-221.
[11]QU Z,GUO Y,JU F R,et al.The algorithm of accelerated cracks detection and extracting skeleton by direction chain code in concrete surface image[J].The Imaging Science Journal,2016,64(3):119-130.
[12]NGUYEN T S,BEGOT S,DUCULTY F,et al.Free-form anisotropy:A new method for crack detection on pavement surface images[C]∥IEEE International Conference on Image Proces-sing.Brussels,Belgium,2011:1069-1072.

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结构森林边缘检测与渗流模型相结合的混凝土表面裂缝检测

Concrete Surface Cracks Detection Combining Structured Forest Edge Detection and Percolation Model

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