Study On Surface Defect Classification Method for Strip Steel Based on MTF-gcForest
Aiming at the uneven distribution and complexity of strip steel surface defects,this paper proposes a strip defect classification method known as MTF-gcForest(Multi-Texture Fusion-gcForest)to ensure the dimensional richness and recognition accuracy of feature extraction.Firstly,the gray-level co-occurrence matrix(GLCM),local binary patterns(LBP),and gray-level run-length matrix(GLRLM)of the strip surface are extracted to fully excavate the texture information of the strip surface.Then,the features are normalized,fused,and finally classified with the gcForest classifier.The experiment compares the performance of single-texture feature and multi-texture feature and evaluates the classification accuracy of various classifiers.The experimental results show that the average accuracy rate based on the MTF-gcForest method reaches 97.22%,which is better than other strip surface defect detection algorithms with significant potential for widespread application.
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