Binary Neighborhood Coordinate Descriptor for Circuit Board Defect Detection
Due to the periodicity of circuit boards, the registration algorithm based on keypoints is less robust in circuit board detection and is prone to misregistration problems. In this paper, the binary neighborhood coordinate descriptor (BNCD) is proposed and applied to circuit board image registration....
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-03-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/6/1435 |
| _version_ | 1797612179330957312 |
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| author | Jiaming Zhang Xuejuan Hu Tan Zhang Shiqian Liu Kai Hu Ting He Xiaokun Yang Jianze Ye Hengliang Wang Yadan Tan Yifei Liang |
| author_facet | Jiaming Zhang Xuejuan Hu Tan Zhang Shiqian Liu Kai Hu Ting He Xiaokun Yang Jianze Ye Hengliang Wang Yadan Tan Yifei Liang |
| author_sort | Jiaming Zhang |
| collection | DOAJ |
| description | Due to the periodicity of circuit boards, the registration algorithm based on keypoints is less robust in circuit board detection and is prone to misregistration problems. In this paper, the binary neighborhood coordinate descriptor (BNCD) is proposed and applied to circuit board image registration. The BNCD consists of three parts: neighborhood description, coordinate description, and brightness description. The neighborhood description contains the grayscale information of the neighborhood, which is the main part of BNCD. The coordinate description introduces the actual position of the keypoints in the image, which solves the problem of inter-period matching of keypoints. The brightness description introduces the concept of bright and dark points, which improves the distinguishability of BNCD and reduces the calculation amount of matching. Experimental results show that in circuit board image registration, the matching precision rate and recall rate of BNCD is better than that of classic algorithms such as scale-invariant feature transform (SIFT) and speeded up robust features (SURF), and the calculation of descriptors takes less time. |
| first_indexed | 2024-03-11T06:37:39Z |
| format | Article |
| id | doaj.art-3d106af83d4c470e934e5fc790bad786 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T06:37:39Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-3d106af83d4c470e934e5fc790bad7862023-11-17T10:45:30ZengMDPI AGElectronics2079-92922023-03-01126143510.3390/electronics12061435Binary Neighborhood Coordinate Descriptor for Circuit Board Defect DetectionJiaming Zhang0Xuejuan Hu1Tan Zhang2Shiqian Liu3Kai Hu4Ting He5Xiaokun Yang6Jianze Ye7Hengliang Wang8Yadan Tan9Yifei Liang10Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, ChinaSino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, ChinaSino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, ChinaLaboratory of Advanced Optical Precision Manufacturing Technology of Guangdong Provincial Higher Education Institute, Shenzhen Technology University, Shenzhen 518118, ChinaSino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, ChinaSino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, ChinaSino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, ChinaSino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, ChinaSino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, ChinaSino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, ChinaSino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, ChinaDue to the periodicity of circuit boards, the registration algorithm based on keypoints is less robust in circuit board detection and is prone to misregistration problems. In this paper, the binary neighborhood coordinate descriptor (BNCD) is proposed and applied to circuit board image registration. The BNCD consists of three parts: neighborhood description, coordinate description, and brightness description. The neighborhood description contains the grayscale information of the neighborhood, which is the main part of BNCD. The coordinate description introduces the actual position of the keypoints in the image, which solves the problem of inter-period matching of keypoints. The brightness description introduces the concept of bright and dark points, which improves the distinguishability of BNCD and reduces the calculation amount of matching. Experimental results show that in circuit board image registration, the matching precision rate and recall rate of BNCD is better than that of classic algorithms such as scale-invariant feature transform (SIFT) and speeded up robust features (SURF), and the calculation of descriptors takes less time.https://www.mdpi.com/2079-9292/12/6/1435defect detectionfeature descriptionfeature matching |
| spellingShingle | Jiaming Zhang Xuejuan Hu Tan Zhang Shiqian Liu Kai Hu Ting He Xiaokun Yang Jianze Ye Hengliang Wang Yadan Tan Yifei Liang Binary Neighborhood Coordinate Descriptor for Circuit Board Defect Detection Electronics defect detection feature description feature matching |
| title | Binary Neighborhood Coordinate Descriptor for Circuit Board Defect Detection |
| title_full | Binary Neighborhood Coordinate Descriptor for Circuit Board Defect Detection |
| title_fullStr | Binary Neighborhood Coordinate Descriptor for Circuit Board Defect Detection |
| title_full_unstemmed | Binary Neighborhood Coordinate Descriptor for Circuit Board Defect Detection |
| title_short | Binary Neighborhood Coordinate Descriptor for Circuit Board Defect Detection |
| title_sort | binary neighborhood coordinate descriptor for circuit board defect detection |
| topic | defect detection feature description feature matching |
| url | https://www.mdpi.com/2079-9292/12/6/1435 |
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