Detecting Defects With Support Vector Machine in Logistics Packaging Boxes for Edge Computing
The accuracy of defects detection for logistics packaging box is a critical factor to ensure the quality of goods under edge computing environment. Now, there are few works on this issue. This paper designs an image acquisition process system and then proposes a novel approach in addressing logistic...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9051700/ |
| _version_ | 1819120744160821248 |
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| author | Xi Yang Mingrui Han Hengliang Tang Qian Li Xiong Luo |
| author_facet | Xi Yang Mingrui Han Hengliang Tang Qian Li Xiong Luo |
| author_sort | Xi Yang |
| collection | DOAJ |
| description | The accuracy of defects detection for logistics packaging box is a critical factor to ensure the quality of goods under edge computing environment. Now, there are few works on this issue. This paper designs an image acquisition process system and then proposes a novel approach in addressing logistics packaging box defect detection (LPDD) on the basis of support vector machine (SVM). Firstly, this paper designs a new mean denoising template and Laplace sharpening template, which are more suitable for logistics packaging based on image preprocessing, image enhancement and other relevant technical theories. Then in the stage of noise removal, this paper proposes an improved morphological method and a gray morphological edge detection algorithm. The edge defect detection of a gray image is carried out by combining the above two methods. Hence, LPDD extracts the features of logistics packaging box by using scale-invariant feature transform (SIFT) algorithm and designs SVM classifiers to classify the logistics package defects. This paper uses a large number of samples to train, learn and test the designed SVM classifier. The simulation results show that the proposed LPDD method can accurately detect two common types of defects in logistics packaging boxes with higher accuracy and less computational costs, which meets the requirements of manufacturers on the classification and recognition of defects in machine vision detection system. |
| first_indexed | 2024-12-22T06:25:32Z |
| format | Article |
| id | doaj.art-52f33f4a647c41fab0439343c8b9e64a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T06:25:32Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-52f33f4a647c41fab0439343c8b9e64a2022-12-21T18:35:51ZengIEEEIEEE Access2169-35362020-01-018640026401010.1109/ACCESS.2020.29845399051700Detecting Defects With Support Vector Machine in Logistics Packaging Boxes for Edge ComputingXi Yang0https://orcid.org/0000-0003-3568-8071Mingrui Han1https://orcid.org/0000-0001-8027-9723Hengliang Tang2https://orcid.org/0000-0001-6747-2369Qian Li3https://orcid.org/0000-0002-7415-9554Xiong Luo4https://orcid.org/0000-0002-1929-8447School of Information, Beijing Wuzi University, Beijing, ChinaSchool of Information, Beijing Wuzi University, Beijing, ChinaSchool of Information, Beijing Wuzi University, Beijing, ChinaSchool of Information, Beijing Wuzi University, Beijing, ChinaBeijing Intelligent Logistics System Collaborative Innovation Center, Beijing, ChinaThe accuracy of defects detection for logistics packaging box is a critical factor to ensure the quality of goods under edge computing environment. Now, there are few works on this issue. This paper designs an image acquisition process system and then proposes a novel approach in addressing logistics packaging box defect detection (LPDD) on the basis of support vector machine (SVM). Firstly, this paper designs a new mean denoising template and Laplace sharpening template, which are more suitable for logistics packaging based on image preprocessing, image enhancement and other relevant technical theories. Then in the stage of noise removal, this paper proposes an improved morphological method and a gray morphological edge detection algorithm. The edge defect detection of a gray image is carried out by combining the above two methods. Hence, LPDD extracts the features of logistics packaging box by using scale-invariant feature transform (SIFT) algorithm and designs SVM classifiers to classify the logistics package defects. This paper uses a large number of samples to train, learn and test the designed SVM classifier. The simulation results show that the proposed LPDD method can accurately detect two common types of defects in logistics packaging boxes with higher accuracy and less computational costs, which meets the requirements of manufacturers on the classification and recognition of defects in machine vision detection system.https://ieeexplore.ieee.org/document/9051700/Package defectslogistics packaging boxsupport vector machine (SVM) |
| spellingShingle | Xi Yang Mingrui Han Hengliang Tang Qian Li Xiong Luo Detecting Defects With Support Vector Machine in Logistics Packaging Boxes for Edge Computing IEEE Access Package defects logistics packaging box support vector machine (SVM) |
| title | Detecting Defects With Support Vector Machine in Logistics Packaging Boxes for Edge Computing |
| title_full | Detecting Defects With Support Vector Machine in Logistics Packaging Boxes for Edge Computing |
| title_fullStr | Detecting Defects With Support Vector Machine in Logistics Packaging Boxes for Edge Computing |
| title_full_unstemmed | Detecting Defects With Support Vector Machine in Logistics Packaging Boxes for Edge Computing |
| title_short | Detecting Defects With Support Vector Machine in Logistics Packaging Boxes for Edge Computing |
| title_sort | detecting defects with support vector machine in logistics packaging boxes for edge computing |
| topic | Package defects logistics packaging box support vector machine (SVM) |
| url | https://ieeexplore.ieee.org/document/9051700/ |
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