Bearing characters recognition system based on LabVIEW

Wang, Hengdi; Yang, Zong-Xiao; Deng, Sier; Erdong, Sun; Wang, Yong

doi:10.1109/cecnet.2011.5768721

Cited by 3 publications

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The existing machine vision literature adopts targeted character recognition methods [34]. Liu [20] trains 50 samples with a small neural network, and it can classify seven classes of characters and defective characters.…”

Section: Character Recognition and Defect Detection Of Characters 31mentioning

confidence: 99%

“…Then it recognizes characters through the moment invariant features of the character edge envelopes. According to the position, division, and normalization features of characters, Wang [34] uses LabVIEW's virtual instrument technology and image processing technology to recognize characters. This paper proposes a character recognition method based on Spatial Pyramid Character Proportion Matching (SPCPM).…”

Section: Character Recognition and Defect Detection Of Characters 31mentioning

confidence: 99%

See 1 more Smart Citation

Segmented Embedded Rapid Defect Detection Method for Bearing Surface Defects

et al. 2021

View full text Add to dashboard Cite

The rapid development of machine vision has prompted the continuous emergence of new detection systems and algorithms in surface defect detection. However, most of the existing methods establish their systems with few comparisons and verifications, and the methods described still have various problems. Thus, an original defect detection method: Segmented Embedded Rapid Defect Detection Method for Surface Defects (SERDD) is proposed in this paper. This method realizes the two-way fusion of image processing and defect detection, which can efficiently and accurately detect surface defects such as depression, scratches, notches, oil, shallow characters, abnormal dimensions, etc. Besides, the character recognition method based on Spatial Pyramid Character Proportion Matching (SPCPM) is used to identify the engraved characters on the bearing dust cover. Moreover, the problem of characters being cut in coordinate transformation is solved through Image Self-Stitching-and-Cropping (ISSC). This paper adopts adequate real image data to verify and compare the methods and proves the effectiveness and advancement through detection accuracy, missing alarm rate, and false alarm rate. This method can provide machine vision technical support for bearing surface defect detection in its real sense.

show abstract

Section: Character Recognition and Defect Detection Of Characters 31mentioning

confidence: 99%

Section: Character Recognition and Defect Detection Of Characters 31mentioning

confidence: 99%