The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
An Effective Method of Weld Defect Detection and Classification Based on Machine Vision
Abstract
In order to effectively identify and classify weld defects of thin-walled metal canisters, a weld defect detection and classification algorithm based on machine vision is proposed in this paper. With the weld defects categorized, a modified background subtraction method based on Gaussian mixture models, is proposed to extract the feature areas of the weld defects. Then, we design an algorithm for weld detection and classification according to the extracted features. Next, by using the weld images sampled by the constructed weld defect detection system on a real-world production line, the parameters of the weld defect classifiers are determined empirically. Experimental results show that the proposed methods can identify and classify the weld defects with more than 95% accuracy rate. Moreover, the weld detection results obtained in the actual production line show that the detection and classification accuracy can reach more than 99%, which means that the system enhanced with the proposed method can meet the requirements for the best real-time and continuous weld defect detection systems available nowadays.
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Adaptive background mixture models for real-time tracking
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Pfinder: real-time tracking of the human body
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Non-parametric Model for Background Subtraction
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Cited by
Surface defect detection of steel strips based on improved YOLOv4
Li, M., Wang, H., & Wan, Z. (2022) · Computers and Electrical Engineering · doi:10.1016/j.compeleceng.2022.108208 · Cited by 138
Gao, Y., Lin, J., Xie, J., & Ning, Z. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3013277 · Cited by 82
Metadata
- Authors
- Sun, J., Li, C., Wu, X.-J., Palade, V., & Fang, W.
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2019-12
- Volume
- 15
- Issue
- 12
- Pages
- 6322-6333
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W2911923560
- Cited by
- 253
- References
- 23
- FWCI
- 12.45
- Type
- journal article
- Language
- en
- Open access
- closed
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