A potential crack region method to detect crack using image processing of multiple thresholding

Cheng Chen, Hyungjoon Seo*, Chang Hyun Jun, Yang Zhao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)


In this paper, a potential crack region method is proposed to detect road pavement cracks by using the adaptive threshold. To reduce the noises of the image, the pre-treatment algorithm was applied according to the following steps: grayscale processing, histogram equalization, filtering traffic lane. From the image segmentation methods, the algorithm combines the global threshold and the local threshold to segment the image. According to the grayscale distribution characteristics of the crack image, the sliding window is used to obtain the window deviation, and then, the deviation image is segmented based on the maximum inter-class deviation. Obtain a potential crack region and then perform a local threshold-based segmentation algorithm. Real images of pavement surface were used at the Su Tong Li road in Suzhou, China. It was found that the proposed approach could give a more explicit description of pavement cracks in images. The method was tested on 509 images of the German asphalt pavement distress (Gap) dataset: The test results were found to be promising (precision = 0.82, recall = 0.81, F1 score = 0.83).

Original languageEnglish
Pages (from-to)1673-1681
Number of pages9
JournalSignal, Image and Video Processing
Issue number6
Publication statusPublished - Sept 2022


  • Filtering
  • Multiple thresholding
  • Pavement crack detection
  • Segmentation


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