计算机科学 ›› 2017, Vol. 44 ›› Issue (1): 300-302.doi: 10.11896/j.issn.1002-137X.2017.01.055

• 图形图像与模式识别 • 上一篇    下一篇

一种基于改进渗流模型的混凝土表面裂缝快速检测算法

瞿中,郭阳,鞠芳蓉   

  1. 重庆邮电大学计算机科学与技术学院 重庆400065;重庆市软件质量保证与测评工程技术研究中心 重庆400065,重庆邮电大学计算机科学与技术学院 重庆400065,重庆邮电大学软件工程学院 重庆400065
  • 出版日期:2018-11-13 发布日期:2018-11-13
  • 基金资助:
    本文受重庆市科委基础科学与前沿技术研究重点项目(cstc2015jcyjB0269),重庆市科委基础科学与前沿技术研究(cstc2014jcyjA1347),重庆市高校优秀成果转化资助

Algorithm of Accelerated Cracks Detection Based on Improved Percolation Model in Concrete Surface Image

QU Zhong, GUO yang and JU Fang-rong   

  • Online:2018-11-13 Published:2018-11-13

摘要: 由于混凝土表面不平整、光照不均、裂缝背景较为复杂等因素的干扰,传统的基于图像处理的裂缝检测方法对裂缝检测的效果不佳,尤其是不清晰和比较细小的裂缝。基于渗流模型的裂缝检测方法充分考虑了裂缝亮度低、形状较为细长的特点,对裂缝的检测效果很好,尤其是图像中的细小裂缝,但是该方法需要大量的处理时间。为了解决上述问题,提出了一种加速渗流处理的算法,该算法通过暗点预提取来减少渗流时需要处理的像素点个数,以此来减少渗流处理时间。实验结果表明,所提算法能明显加快渗流处理的速度,并且精确率基本保持不变。

关键词: 图像处理,裂缝检测,重叠分块,渗流模型,预提取,去噪

Abstract: Due to concrete surface roughness,uneven illumination,shadows,complex background and other disruptive factors,the traditional concrete crack detection method based on image processing cannot accurately detect concrete cracks,especially unclear cracks and some tiny cracks.Crack detection method based on percolation model which fully considered the low brightness and slenderness features of cracks can accurately detect unclear and tiny cracks.But this method is time-consuming.In order to solve these problems,an improved algorithm of image crack inspection based on percolation model was proposed in the article,which can reduce processing time through reducing the number of percolated pixels.Experimental results show that the proposed algorithm in this article can significantly accelerate crack detection and maintain a high detection precision.

Key words: Image process,Crack detection,Overlapped block,Percolation model,Pre-extraction,De-noise

[1] ADU-GYAMFI Y O,OKINE N O A,Garateguy G,et al.Multiresolution Information Mining for Pavement Crack Image Analysis[J].Journal of Computing in Civil Engineering,2012,26(6):741-749 .
[2] HASSAN M,MAN S H,CHENG Z N,et al.Development of Energy Harvested Wireless Sensing Node for Structural Health Monitoring[C]∥Mixed-Signals,Sensors and Systems Test Workshop (IMS3TW).Taipei,Taiwan,2012.
[3] GRUZMAN I S.Using the model of a mixture of a uniform distribution and a von Mises distribution for segmentation of anisotropic images[J].Optoelectronics Instrumentation & Data Processing,2014,50(2):118-124.
[4] HATADA T,SAITOH F.Crack detection method for drain by using directional smoothing[J].IEEJ Transactions on Electronics Information & Systems,2007,127(2):241-246.
[5] FUJITA Y,MITANI Y,HAMAMOTO Y.A Method for Crack detection on a Concrete Structure[C]∥18th International Conference on Pattern Recognition.Hong Kong,2006.
[6] FUJITA Y,MITANI Y,HAMAMOTO Y.A robust method for automatically detecting cracks on noisy concrete surfaces[C]∥Next-Generation Applied Intelligence.22nd International Conference on Industrial,Engineering and Other Applications of Applied Intelligent Systems IEA/AIE.Tainan, Taiwan,2009.
[7] FUJITA Y,HAMAMOTO Y.A robust automatic crack detection method from noisy concrete surfaces[J].Machine Vision and Applications,2011,22(2):245-254.
[8] YAMAGUCHI T,HASHIMOTO S.Image processing based on percolation model[J].IEICE transactions on information and systems,2006,E89-D(7):2044-2052.
[9] YAMAGUCHI T,HASHIMOTO S.Automated crack detection for concrete surface image using percolation model and edge information[C]∥IECON 2006-32nd Annual Conference on IEEE Industrial Electronics.Paris France,2006.
[10] YAMAGUCHI T,HASHIMOTO S.Improved percolation-based method for crack detection in concrete surface images[C]∥International Conference on Pattern Recognition.Florida USA,2008.
[11] YAMAGUCHI T,NAKAMURA S,SA EGISA R,et al.Image-Based Crack Detection for Real Concrete Surfaces[J].IEEJ Transactions on Electrical and Electronic Engineering,2008,3(1):128-135.
[12] 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.
[13] QU Zhong,LIN Li-dan,YANG Guo,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.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!