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http://dx.doi.org/10.6109/jkiice.2021.25.12.1853

Machine Parts(O-Ring) Defect Detection Using Adaptive Binarization and Convex Hull Method Based on Deep Learning  

Kim, Hyun-Tae (Major of Applied Software Engineering, Dongeui University,)
Seong, Eun-San (Department of Digital Media Engineering, Dong-Eui University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.12, 2021 , pp. 1853-1858 More about this Journal
Abstract
O-rings fill the gaps between mechanical parts. Until now, the sorting of defective products has been performed visually and manually, so classification errors often occur. Therefore, a camera-based defect classification system without human intervention is required. However, a binarization process is required to separate the required region from the background in the camera input image. In this paper, an adaptive binarization technique that considers the surrounding pixel values is applied to solve the problem that single-threshold binarization is difficult to apply due to factors such as changes in ambient lighting or reflections. In addition, the convex hull technique is also applied to compensate for the missing pixel part. And the learning model to be applied to the separated region applies the residual error-based deep learning neural network model, which is advantageous when the defective characteristic is non-linear. It is suggested that the proposed system through experiments can be applied to the automation of O-ring defect detection.
Keywords
O-ring; Adaptive binarization; Convex hull; Deep learning;
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