01764683

Component

https://doi.org/10.1177/03611981211002203

Cracks considerably reduce the life span of pavement surfaces. Currently, there is a need for the development of robust automated distress evaluation systems that comprise a low-cost crack detection method for performing fast and cost-effective roadway health monitoring practices. Most of the current methods are costly and have labor-intensive learning processes, so they are not suitable for small local-level projects with limited resources or are only usable for specific pavement types.This paper proposes a new method that uses an adapted version of the weighted neighborhood pixels segmentation algorithm to detect cracks in 2-D pavement images. The method uses the Gaussian cumulative density function (CDF) as the adaptive threshold to overcome the drawback of fixed thresholds in noisy environments. The proposed algorithm was tested on 300 images containing a wide range of noise representative of various pavement noise conditions. The method proved to be time and cost-efficient as it took less than 3.15?s per 320?×?480 pixels image for a Xeon (R) 3.70?GHz CPU processor to generate the detection results. This makes the proposed method a perfect choice for county-level pavement maintenance projects requiring cost-effective pavement crack detection systems. The validation results were promising for the detection of medium to severe-level cracks (precision?=?79.21%, recall?=?89.18%, and F1 score?=?83.90%).

The data for this study is available and can be accessed through: https://drive.google.com/file/d/1U8Ie-H-JYZUU5lSN0IXEiNf_S64g4kv8/view?usp=sharing (Safaei, Smadi, Safaei and Masoud, 2021). © National Academy of Sciences: Transportation Research Board 2021.

TRBAM-21-02055

English

pp 370-381

2021-9

Transportation Research Record: Journal of the Transportation Research Board

Digital/other

Figures; Photos; References (59) ; Tables

Algorithms; Cost effectiveness; Detection and identification technologies; Image analysis; Machine learning; Pavement cracking; Pavement management systems; Structural health monitoring

Data and Information Technology; Finance; Highways; Maintenance and Preservation; Pavements

TRIS, TRB, ATRI

Feb 1 2021 12:09PM