Computer Science ›› 2018, Vol. 45 ›› Issue (11): 288-291.doi: 10.11896/j.issn.1002-137X.2018.11.046

• Graphics, Image & Pattern Recognition • Previous Articles     Next Articles

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

QU Zhong, JU Fang-rong, CHEN Si-qi   

  1. (School of Software Engineering,Chongqing University of Posts & Telecommunications,Chongqing 400065,China)
  • Received:2017-10-31 Published:2019-02-25

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

CLC Number: 

  • TP391.41
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