Research on Surface Defect Detection Technology Based on the Zonal and Time-Sharing Computational Imaging

In order to solve the misjudgment problem of different information in common gray images due to the very similar features such as morphology and gray value, and to avoid the high cost of using the 3-D sensor in defect detection, a zonal and time-sharing computational imaging (ZTSCI) is proposed in t...

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Bibliographic Details
Published in:IEEE access 2022, Vol.10, p.79574-79583
Main Authors: Li, Chen, Yin, Yongjing, Yuan, Bin, Li, Xiangqing
Format: Article
Language:English
Subjects:
Computing time
Defect detection
feature enhancement
Image enhancement
Imaging
Interference
Lighting
Machine vision
Mathematical analysis
Recognition
Surface defects
Surface layers
Surface morphology
Surface texture
Surface topography
Surface treatment
Time sharing
zonal lighting
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Summary:In order to solve the misjudgment problem of different information in common gray images due to the very similar features such as morphology and gray value, and to avoid the high cost of using the 3-D sensor in defect detection, a zonal and time-sharing computational imaging (ZTSCI) is proposed in this paper. Firstly, a four-zone and time-sharing exposure visual imaging system is constructed by monocular telecentric imaging and lights in different directions. Then, according to the gray images under four groups of illumination conditions, and the direction vectors of incident lights, five feature images are calculated, such as fused images of maximum and average, enhanced images of normal vector, gradient and relative height. On the one hand, the image background fluctuation caused by the calculation error introduced by the ideal hypothesis is weakened, and the interference of surface texture to defect detection is reduced. On the other hand, the difference of easily-confused information can be increased in different feature spaces. In this paper, the ZTSCI is further applied to defect detection on the surface of new energy batteries. It solves the problem that defects and interference information are difficult to distinguish in gray images captured with common machine vision methods, reduces the difficulty of subsequent defect recognition, and lays a foundation for improving the accuracy of recognition.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3163726