An automatic system for bearing surface tiny defect detection based on multi-angle illuminations

Liu, Bin; Yang, Yiqian; Wang, Shangshang; Bai, Yang; Zhang, Jianxin

doi:10.1016/j.ijleo.2020.164517

Cited by 20 publications

(18 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It can be seen from the figure that the false alarm rate of the paper [20] was the highest, reaching 4%. The false alarm rate of paper [21] was relatively low, only 2.06%, but its missing alarm rate was 6%, which is not allowed in actual detection.…”

Section: Methodsmentioning

confidence: 92%

“…Therefore, we have further tightened the basis for determining defects in each step of SERDD and finally achieved zero missing alarm results. We sorted out the experimental data in the paper [20,21] and compared them with our results, as shown in Figure 20. Figure 20.…”

Section: Methodsmentioning

confidence: 99%

“…Liu [20] proposes a band expansion method based on character width to solve the problem of the character being cut. This method restores the cut characters but retains the characters' cut parts and may cut another character again.…”

Section: Image Self-stitching-and-cropping (Issc)mentioning

confidence: 99%

“…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. Shen [21] divides each character into 3 × 3 cells, calculates the proportion of the character part in each small cell as a feature vector, and then recognizes the character by 2-norm of the difference between the template feature vector and this feature vector.…”

Section: Character Recognition and Defect Detection Of Characters 31mentioning

confidence: 99%

“…However, the two methods are not mutually contradictory. For instance, Liu [20] uses methods of image processing and detection to filter, locate, segment, and detect bearing images but uses the neural network method for reasoning on the character recognition on the bearing dust cover. Shen [21] also adopts image processing methods for filtering, segmentation, dimension detection, and character recognition of bearing images.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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: Methodsmentioning

confidence: 92%

Section: Methodsmentioning

confidence: 99%

Section: Image Self-stitching-and-cropping (Issc)mentioning

confidence: 99%