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.

The textile industry holds a central position in India's economy, contributing substantially to both employment and GDP. Despite technological advancements, maintaining stringent quality standards remains a persistent challenge due to defects such as cracks, stains, and inconsistencies in fabrics. Traditional manual inspection methods, while effective to a degree, are labor-intensive, time-consuming, and prone to human error. This paper proposes an innovative approach to address these challenges through the application of machine learning and computer vision techniques in fabric defect detection. Specifically, the research focuses on integrating advanced texture feature extraction methods—Gray-Level Co-occurrence Matrix (GLCM), Local Binary Patterns (LBP), and Histogram of Oriented Gradients (HOG)—with a robust classification framework using Bayesian optimized Random Forest. The methodology emphasizes efficient feature selection via Minimum Redundancy Maximum Relevance (MRMR), enhancing the system's accuracy and efficiency. By leveraging a comprehensive dataset from Kaggle encompassing diverse fabric defects, the proposed system aims to significantly improve defect detection accuracy, reduce manual intervention, and ensure consistent product quality across textile manufacturing processes. The highest accuracy achieved in the evaluation is 99.52%.