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학술지 Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing
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저자
고동범, 강성주, 김현석, 이원곡, 배유석, 박정민
발행일
202111
출처
Applied Sciences, v.11 no.21, pp.1-18
ISSN
2076-3417
출판사
MDPI
DOI
https://dx.doi.org/10.3390/app112110376
초록
This paper introduces and implements an efficient training method for deep learning?? based anomaly area detection in the depth image of a tire. A depth image of 16 bit integer size is used in various fields, such as manufacturing, industry, and medicine. In addition, the advent of the 4th Industrial Revolution and the development of deep learning require deep learning?밷ased problem solving in various fields. Accordingly, various research efforts use deep learning technology to detect errors, such as product defects and diseases, in depth images. However, a depth image expressed in grayscale has limited information, compared with a three?릀hannel image with potential colors, shapes, and brightness. In addition, in the case of tires, despite the same defect, they often have different sizes and shapes, making it difficult to train deep learning. Therefore, in this paper, the four?릗tep process of (1) image input, (2) highlight image generation, (3) image stacking, and (4) image training is applied to a deep learning segmentation model that can detect atypical defect data. Defect detection aims to detect vent spews that occur during tire manufacturing. We compare the training results of applying the process proposed in this paper and the general training result for experiment and evaluation. For evaluation, we use intersection of union (IoU), which compares the pixel area where the actual error is located in the depth image and the pixel area of the error inferred by the deep learning network. The results of the experiment confirmed that the proposed methodology improved the mean IoU by more than 7% and the IoU for the vent spew error by more than 10%, compared to the general method. In addition, the time it takes for the mean IoU to remain stable at 60% is reduced by 80%. The experiments and results prove that the methodology proposed in this paper can train efficiently without losing the information of the original depth data.
KSP 제안 키워드
Actual error, Deep learning network, Defect Detection, Depth Data, Depth image, Different sizes, General method, Image generation, Image stacking, Industrial Revolution, Limited information
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