Random forest steel defect detection algorithm based on PCA
Steel materials can have defects such as scratches and stains during the raw material production,smelting,and processing processes.Defect detection is an essential aspect of controlling product quality and production efficiency in the manufacturing process.Traditional classification algorithms are inefficient for defect detection.Therefore,a random forest algorithm based on PCA and SMOTE is proposed in this work.Through SMOTE,PCA,and random forest algorithm,effective detection of steel defects has been achieved.Experimental results demonstrate that compared to traditional classification algorithms such as Logistic Regression and Decision Trees,the proposed method significantly improves defect detection efficiency.