Robust concrete crack recognition based on improved image segmentation and machine learning

Qiancheng Zhao; Jiang Shao; Tianlong Yang

doi:10.1117/12.2511359

7 March 2019 Robust concrete crack recognition based on improved image segmentation and machine learning

Qiancheng Zhao, Jiang Shao, Tianlong Yang

Proceedings Volume 11053, Tenth International Symposium on Precision Engineering Measurements and Instrumentation; 110531K (2019) https://doi.org/10.1117/12.2511359
Event: 10th International Symposium on Precision Engineering Measurements and Instrumentation (ISPEMI 2018), 2018, Kunming, China

Abstract

This paper presents an automatical crack recognition approach. Compared with the existing methods, it has a significant increase in robustness and efficiency when faced with widely varying field conditions. Inherent characteristics of crack images are exploited using proportional segmentation, combined with robust feature extraction to improve machine learning classifier performance. Experiments show that this method perform well in crack images recognition across different concrete conditions.

Citation Download Citation

Qiancheng Zhao, Jiang Shao, and Tianlong Yang "Robust concrete crack recognition based on improved image segmentation and machine learning", Proc. SPIE 11053, Tenth International Symposium on Precision Engineering Measurements and Instrumentation, 110531K (7 March 2019); https://doi.org/10.1117/12.2511359

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