Kirigami Art Effect based on Multi Verse Optimization Algorithm

In the paper manufacturing business, automated flaw identification is a crucial quality control method. The purpose of this activity is to locate and identify creases, cracks, holes, and stains that occur during the making of wrapping paper. Experiments are conducted to validate the proposed Multi-Verse Optimization Algorithm (MVOA). In order to reduce background noise, the sampled collected paper pictures are pre-processed using the greyscale approach and Gaussian filtering. The image defects are then segmented using an MVOA algorithm that has been improved by the morphological process. A fused multi-feature of form and texture characteristics is proposed to categorize four categories of faults. Four statistics from the Gray-Level Co-Occurrence Matrix (GLCM) technique are used as texture features, and the defect’s size and aspect ratio are computed as shape features. The Support Vector Machine (SVM) classifier is used for classification to classify the image. According to the results it illustrated that the suggested method achieved better performance in terms in accuracy of 99.52% when compared to existing methods namely.