The Research on Surface Defect Classification of Strip Steel Based on IGWO-SVM
To improve the accuracy of surface defect classification for strip steel,a strip steel image classification method based on IGWO-SVM is proposed.The paper first introduced chaotic sequences,elite opposition-based learning strategy,and dynamic nonlinear convergence factor to design the Improved Gray Wolf Optimization Algorithm(IGWO).IGWO is used to optimize the parameters of the Support Vector Machine(SVM),and then the optimized SVM is used to classify surface defect images of steel strips.The paper used 6 benchmark functions and surface defect images of strip steel for simulation experiments.The experimental results showed that IGWO has higher accuracy and convergence,and optimized support vector machine classification with IGWO can effectively improve classification accuracy.
improved gray wolf optimization algorithmsupport vector machinedefect classificationelite opposition-based learning
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