Efficient Road Crack Detection Based on an Adaptive Pixel-Level Segmentation Algorithm
| Autor: | Omar Smadi, Nima Safaei, Babak Safaei, Arezoo Masoud |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
050210 logistics & transportation
Pixel Life span Computer science business.industry Mechanical Engineering 05 social sciences 0211 other engineering and technologies 02 engineering and technology 021105 building & construction 0502 economics and business Computer vision Segmentation Artificial intelligence business Civil and Structural Engineering |
| Zdroj: | Transportation Research Record: Journal of the Transportation Research Board. 2675:370-381 |
| ISSN: | 2169-4052 0361-1981 |
| Popis: | 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 F1score = 83.90%). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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