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Defect detection in hot stamping process printed matter by beluga optimized support vector machine with opposition-based learning

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Abstract

The high surface reflectivity and concave-convex of hot stamping process printed matters lead to poor defect detection accuracy. To solve this problem, this paper proposes a defect detection model (OBL-BWO-SVM) for hot stamping process printed matters based on oppositional learning (OBL) beluga optimized (BWO) support vector machine (SVM). First, in terms of image acquisition, a new light source illumination method is proposed for the own characteristics of hot stamping, the gray level covariance matrix (GLCM) is applied to extract the image information to build a dataset, and principal component analysis (PCA) is applied to reduce the dimensionality of the dataset. Then, SVM is used as the base model and the category weights of SVM are adjusted for the sample data category imbalance problem. Then, the C and gamma parameters of SVM are optimized using OBL-BWO. Finally, 14400 images are used as the dataset, in which the proportions of defect-free samples and defective samples are 48.9% and 51.1%, respectively, and are divided into the training set and the test set according to the ratio of 7:3. The models in this paper and the commonly used models are tested on this dataset. The experimental results show that the accuracy of the model proposed in this paper is 96.11%, precision is 94.61%, recall is 95.75%, and F1 score is 95.18%. Compared with the commonly used methods, the method proposed in this paper has higher classification accuracy and can improve the accuracy of printed matter defect detection during hot stamping printing.

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No datasets were generated or analysed during the current study.

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Acknowledgements

The research work described in this paper was funded by a basic public welfare research project conducted by the Wenzhou Municipal Bureau of Science and Technology (Project No. G20240062).

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X.R. and Jq.L. wrote the main manuscript text and Yd.Y.and Lh.P and Zc.S prepared figures and Table. All authors reviewed the manuscript.

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Correspondence to Jianqiang Li.

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Ru, X., Yao, Y., Li, J. et al. Defect detection in hot stamping process printed matter by beluga optimized support vector machine with opposition-based learning. SIViP 19, 93 (2025). https://doi.org/10.1007/s11760-024-03681-5

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