A Novel Feature Extraction Method Based on Legendre Multi-Wavelet Transform and Auto-Encoder for Steel Surface Defect Classification

Effective steel surface defect classification with low computational cost is essential for online quality inspection. The challenge of this task is large intra-class differences and unclear inter-class distances shown in various surface defects. This paper proposes a novel feature extraction method based on Legendre multi-wavelet transform and Auto-Encoder network (LWT-AE) for effectively recognizing the steel surface defects categories. More precisely, the finite element approximation theory is implemented to address the strong capacity of LW bases with various regularities and vanish moments for thoroughly matching the complex geometry characteristics of the steel surface defects, which provides strict theory foundation for the feature extraction in LWT frequency domain without losing any defect information due to its orthogonality. Then, the statistical and texture parameters are utilized to sparsely extract the defect features from LWT frequency domain, resulting in removing the redundancy components corresponding to the defects. AE network is utilized to further reduce the dimension of the extracted features and automatically select the most valuable defect features. Furthermore, two classifiers (SVM and BPNN) are used to rectify the generalization ability of the proposed method. Finally, extensive experiments are conducted on two datasets to verify the effectiveness and robustness of the proposed method. The highest classification accuracies 99.44% and 95.37% compared with other methods are attained on the NEU-CLS dataset and X-SDD dataset, respectively. To summary, the proposed method not only has simple structure, but also can reliably identify different types of the steel surface defects, which provides a suitable online application technique for the actual steel surface defect classification.