Multi-Stream Deep Convolutional Neural Network for PET Preform Surface Defects Detection and Classification
Due to the influence of technology factors, various defects will appear in the production process of PET (Polyethylene Terephthalate) preform, and affect the PET preform quality. In order to meet the requirements of the quality inspection efficiency and accuracy for PET preform, a novel multi-stream...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IEEE
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/a812badf342d42c1a5d6dacfc27338b7 |
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| Sumario: | Due to the influence of technology factors, various defects will appear in the production process of PET (Polyethylene Terephthalate) preform, and affect the PET preform quality. In order to meet the requirements of the quality inspection efficiency and accuracy for PET preform, a novel multi-stream deep CNN (Convolutional Neural Network) model is performed to effectively identify and classify PET preform surface defects for the first time in this paper. In recent years, the method based on multi-stream feature fusion has a good application prospect in defect detection. In order to solve the problem of PET preform surface defects detection and classification, we study the deep CNN structure and propose a multi-stream deep network structure, which makes full use of the rich multi-feature information from different network structures. Two different deep CNNs are respectively trained to extract features from the original image, and one of them is trained to extract feature from the corresponding gradient image. We adopt wavelet transform fusion strategy to realize feature fusion and input them into ECOC-SVM (a classified Error Correcting Output Codes model using Support Vector Machines as binary learner) model for detection and classification. Using Bayesian optimization function to optimize the hyperparameters is performed to choose the best performance configuration. Through experimental analyses, the higher detection accuracy of the proposed method is 98.5%, and it is proved that the proposed model has good convergence, accuracy, stability and generalization ability. |
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