计算机科学 ›› 2014, Vol. 41 ›› Issue (11): 297-300.doi: 10.11896/j.issn.1002-137X.2014.11.058

• 图形图像与模式识别 • 上一篇    下一篇

形态学与区域延伸相结合的图像裂缝检测算法研究

瞿中,林丽丹,郭阳   

  1. 重庆邮电大学计算机科学与技术学院 重庆400065;重庆邮电大学移通学院 重庆401520;重庆邮电大学计算机科学与技术学院 重庆400065;重庆邮电大学计算机科学与技术学院 重庆400065
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受重庆市科委自然科学基金计划项目(2010BB2399)资助

Algorithm of Image Crack Detection Based on Morphology and Region Extends

QU Zhong,LIN Li-dan and GUO Yang   

  • Online:2018-11-14 Published:2018-11-14

摘要: 在复杂背景下,由于光照不均、混凝土气泡、阴影等噪声干扰,导致路面裂缝误检并存在不同程度的断裂。为了解决此问题并实现路面裂缝的精确检测,提出一种结合数学形态学和区域延伸的裂缝检测算法。该算法首先用形态学对自然条件下采集的路面图像进行预处理,并结合Canny边缘检测和形态学来对裂缝进行粗略检测,然后用区域延伸算法对裂缝进行精确检测,最后对检测裂缝进行后处理。实验结果表明,提出的算法能够对图像裂缝进行高效、精确的检测。

关键词: 裂缝检测,预处理,Canny边缘检测,形态学,区域延伸

Abstract: In a complex background,the interference of uneven illumination,concrete bubbles,shadows and other noise,results in false detection of cracks and varying degrees of crack fracture.To solve these problems and implement detecting cracks accurately,an algorithm of crack detection which combines mathematical morphology and region extends was proposed in this paper.Firstly,images collected on the natural conditions are preprocessed using the method of morpho-logy,and then Canny edge detection and morphology are combined to achieve the coarse detection of cracks.Secondly,the regional extension algorithm is adopted for accurate detection.Finally,the cracks are processed to make them natural.Experimental results show that the proposed algorithm can detect the cracks on concrete pavement cracks accurately and efficiently.

Key words: Crack detection,Preprocess,Canny edge detection,Morphology,Region extends

[1] 王平让,黄宏伟,薛亚东.基于图像局部网格特征的隧道衬砌裂缝自动识别[J].岩石力学与工程学报,2012,31(5):991-999
[2] Abdel-Qader I,Abudayyeh O,Kelly M E.Analysis of edge-detection techniques for crack identification in bridges[J].Journal of Computing in Civil Engineering,2003,17(4):255-263
[3] Fujita Y,Hamamoto Y.A robust automatic crack detectionmethod from noisy concrete surfaces[J].Machine Vision and Applications,2011,22(2):245-254
[4] Rathod V R,Anand R S.A comparative study of different segmentation techniques for detection of flaws in NDE weld images[J].Journal of Nondestructive Evaluation,2012,31(1):1-16
[5] Gunkel C,Stepper A,Müller A C,et al.Micro crack detection with Dijkstra’s shortest path algorithm[J].Machine Vision and Applications,2012,23(3):589-601
[6] Yamaguchi T,Hashimoto S.Fast crack detection method forlarge-size concrete surface images using percolation-based image processing[J].Machine Vision and Applications,2010,21(5):797-809
[7] Landstrom A,Thurley M J.Morphology-based crack detection for steel slabs[J].IEEE Journal of Selected Topics in Signal Processing,2012,6(7):866-875
[8] Jahanshahi M R,Masri S F.Adaptive vision-based crack detection using 3D scene reconstruction for condition assessment of structures[J].Automation in Construction,2012,2:567-576
[9] 朱平哲,黎蔚.基于主动生长的断裂裂缝块的连接方法[J].计算机应用,2011,31(12):3382-3384
[10] 林伊.车载路面裂缝检测图像处理系统的设计与实现[D].武汉:华中科技大学,2011
[11] 程仁贵,刘书忻.基于边缘检测的影像多线自动测量算法[J].重庆理工大学学报:自然科学版,2013,27(2):89-92

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