Optimized Classification of Steel Surface Defects via Hybrid Features and Neighborhood Component Analysis

This paper establishes a new process of surface defect detection of steel products with both integrated image processing and image vision capabilities. The approach which incorporates Multi-Scale Local Binary Pattern (MSLBP), Dual-Tree Complex Wavelet Transform (DTCWT), and Gabor Wavelet in extracting features, whilst the Neighborhood Component Analysis (NCA) approach is in selecting the features. Ensemble AdaBoost is employed as a comparative baseline classifier and the final defect detection performance is presented in the Enhanced Snake Optimized Support Vector Machines (ESO-SVM) model. The suggested approach is superior to the classical methods, as the results of the experiments show 98.8 percent accuracy and 98.5 percent F1-score at the process of detecting fine and irregular defects under different production conditions. The system improves reliability and scalability of automatic defect detection thus increasing the quality of steel products and decreasing wastes.