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Comparison and Analysis of Several Clustering Algorithms for Pavement Crack Segmentation Guided by Computational
Dan Wang1,2,3, Zaijun Zhang1,2, Jincheng Zhou2,3,4
1School of Mathematics and Statistics, Qiannan Normal University for Nationalities, Duyun 558000, China.
Computational Intelligence and Neuroscience
|September 13, 2022
Summary
This study compares spatial clustering algorithms for concrete pavement crack segmentation. Mean-shift clustering performed best in noisy conditions, offering insights for pavement maintenance.
Area of Science:
- Civil Engineering
- Computer Science
- Materials Science
Background:
- Cracks in concrete pavement significantly impact structural integrity.
- Effective pavement crack segmentation is crucial for maintenance and safety.
Purpose of the Study:
- To evaluate and compare the performance of various spatial clustering algorithms for pavement crack segmentation.
- To provide data-driven references for current pavement maintenance strategies.
- To analyze algorithm performance under varying conditions, including noisy images.
Main Methods:
- Spatial clustering algorithms were applied to segment pavement cracks.
- Performance was evaluated using Normalized Mutual Information (NMI) and Rand Index (RI).
- Experiments included analysis of crack photos with and without added Gaussian noise.
Main Results:
- Adding Gaussian noise significantly degraded the segmentation performance of most clustering algorithms.
- The mean-shift clustering algorithm demonstrated the most effective de-noising capabilities based on NMI.
- Some clustering algorithms experienced substantial performance decreases when subjected to noise.
Conclusions:
- Spatial clustering algorithm performance is sensitive to image noise in pavement crack segmentation.
- Mean-shift clustering offers superior robustness and de-noising for pavement crack analysis.
- Findings guide the selection of appropriate algorithms for effective pavement maintenance.
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