基于二维主成分分析算法的混凝土裂缝检测研究

Research on crack detection of concrete based on two-dimensional principal component analysis algorithm

摘要:

针对盾构管片混凝土的裂缝检测任务,提出一种基于L1范数和F范数的二维主成分分析(two-dimensional principal component analysis,2DPCA) 算法。考虑到实际工程中异常值干扰问题的重要性,采用L1范数度量以降低特征提取算法对异常值的敏感性。同时,采用F范数的度量来减小算法的重构误差,从而增强重构性能并提升裂缝标记的准确性。对混凝土裂缝图像进行测试,结果表明:所提算法对盾构管片的混凝土裂缝检测具有较好的识别和标记效果,最高识别率可达90.42%。此外,在不同实验条件下对混凝土裂缝进行检测,结果表明,所提算法具有较强的抗噪能力。最后,将该算法应用在人脸识别领域,实验结果表明所提算法仍具有较强鲁棒性与实际应用性。综上所述,采用2DPCA的算法策略在混凝土裂缝检测中具有良好的适用性,未来仍可持续探索相关改进算法。

Abstract:

A two-dimensional principal component analysis (2DPCA) algorithm based on the L1-norm and F-norm is proposed for detecting concrete cracks in shield tunnel segments.Given the significance of addressing outlier interference in practical engineering,the L1-norm metric is adopted to reduce the sensitivity of the feature extraction algorithm to outliers.Simultaneously,the F-norm metric is used to minimize the reconstruction error,thereby enhancing the reconstruction performance and improving the accuracy of crack labeling.Tests on concrete crack images demonstrate that the proposed algorithm achieves excellent recognition and labeling performance,with a maximum recognition rate of 90.42%.Furthermore,experiments under various conditions confirm the algorithm′s strong noise resistance.Finally,the algorithm is applied to the field of face recognition,and the experimental results further validate its robustness and practical applicability.In conclusion,the strategy of employing 2DPCA algorithms shows promising applicability for concrete crack detection,and future research can focus on further refining and enhancing these algorithms.