Research on Surface Defects Classification for PET Preform by Fusing Multi-Scale Features
Product quality control is an important component of PET preform production process. In order to improve the accuracy and reliability of PET preform detection, reduce the cost of product quality defects, and improve the overall level of product quality, this paper proposes a surface defect detection...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11082268/ |
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| author | Chunmei Duan Taochuan Zhang Lei Han Huilin Tan |
| author_facet | Chunmei Duan Taochuan Zhang Lei Han Huilin Tan |
| author_sort | Chunmei Duan |
| collection | DOAJ |
| description | Product quality control is an important component of PET preform production process. In order to improve the accuracy and reliability of PET preform detection, reduce the cost of product quality defects, and improve the overall level of product quality, this paper proposes a surface defect detection method for PET preform based on multi-scale feature fusion. Multi-scale features fusion combines features from different scales to produce more accurate and robust feature representations, which improve the accuracy, stability and adaptability of PET preform detection model. This feature processing model constructs a three-branch network, generating feature maps of different sizes. Then, the low-level feature, the middle-level feature and the high-level feature are fused to obtain a PET preform feature map based on multi-scale feature fusion by using a maximum value fusion strategy. The original image and feature map of the PET preform are respectively input into the deep convolutional neural network for feature extraction and fusion. And finally the fusion features are input into the ECOC-SVM to achieve surface defect classification of the PET preform. The machine vision platform is designed for collecting and detecting PET preform data. By importing the model designed in this paper, surface defect recognition and defect category judgment of PET preform can be achieved. Through experimental data analysis, the higher testing detection accuracy of this method is 98.8%, with an average testing accuracy of 97.1%. |
| format | Article |
| id | doaj-art-77e76707b15b43bea7fb82f90faf1a2e |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-77e76707b15b43bea7fb82f90faf1a2e2025-08-20T03:22:19ZengIEEEIEEE Access2169-35362025-01-011313431213432410.1109/ACCESS.2025.358955911082268Research on Surface Defects Classification for PET Preform by Fusing Multi-Scale FeaturesChunmei Duan0https://orcid.org/0000-0002-0924-3784Taochuan Zhang1https://orcid.org/0000-0002-0489-8562Lei Han2https://orcid.org/0009-0007-7023-3693Huilin Tan3https://orcid.org/0009-0006-2524-5719School of Mechanical Engineering, Guangdong Ocean University, Zhanjiang, ChinaSchool of Mechanical Engineering, Guangdong Ocean University, Zhanjiang, ChinaSchool of Automobile and Transportation Engineering, Guangdong Polytechnic Normal University, Guangzhou, ChinaCollege of Information Science and Engineering, Shaoyang University, Shaoyang, ChinaProduct quality control is an important component of PET preform production process. In order to improve the accuracy and reliability of PET preform detection, reduce the cost of product quality defects, and improve the overall level of product quality, this paper proposes a surface defect detection method for PET preform based on multi-scale feature fusion. Multi-scale features fusion combines features from different scales to produce more accurate and robust feature representations, which improve the accuracy, stability and adaptability of PET preform detection model. This feature processing model constructs a three-branch network, generating feature maps of different sizes. Then, the low-level feature, the middle-level feature and the high-level feature are fused to obtain a PET preform feature map based on multi-scale feature fusion by using a maximum value fusion strategy. The original image and feature map of the PET preform are respectively input into the deep convolutional neural network for feature extraction and fusion. And finally the fusion features are input into the ECOC-SVM to achieve surface defect classification of the PET preform. The machine vision platform is designed for collecting and detecting PET preform data. By importing the model designed in this paper, surface defect recognition and defect category judgment of PET preform can be achieved. Through experimental data analysis, the higher testing detection accuracy of this method is 98.8%, with an average testing accuracy of 97.1%.https://ieeexplore.ieee.org/document/11082268/PET preformmulti-scale featuresfeatures fusionECOC-SVMdetection and classification |
| spellingShingle | Chunmei Duan Taochuan Zhang Lei Han Huilin Tan Research on Surface Defects Classification for PET Preform by Fusing Multi-Scale Features IEEE Access PET preform multi-scale features features fusion ECOC-SVM detection and classification |
| title | Research on Surface Defects Classification for PET Preform by Fusing Multi-Scale Features |
| title_full | Research on Surface Defects Classification for PET Preform by Fusing Multi-Scale Features |
| title_fullStr | Research on Surface Defects Classification for PET Preform by Fusing Multi-Scale Features |
| title_full_unstemmed | Research on Surface Defects Classification for PET Preform by Fusing Multi-Scale Features |
| title_short | Research on Surface Defects Classification for PET Preform by Fusing Multi-Scale Features |
| title_sort | research on surface defects classification for pet preform by fusing multi scale features |
| topic | PET preform multi-scale features features fusion ECOC-SVM detection and classification |
| url | https://ieeexplore.ieee.org/document/11082268/ |
| work_keys_str_mv | AT chunmeiduan researchonsurfacedefectsclassificationforpetpreformbyfusingmultiscalefeatures AT taochuanzhang researchonsurfacedefectsclassificationforpetpreformbyfusingmultiscalefeatures AT leihan researchonsurfacedefectsclassificationforpetpreformbyfusingmultiscalefeatures AT huilintan researchonsurfacedefectsclassificationforpetpreformbyfusingmultiscalefeatures |