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.