Abstract
In the process of toothbrush production, the hair implanter is usually used to implant the brush hair. It is easy to miss the implant in the process of wool implantation, resulting in defective toothbrushes. In the process of subsequent tensile stress testing, the brush that has not reached the standard will also have the defect of the tooth brush hair in this process. At present, most toothbrush manufacturers still use manual to detect toothbrush hair loss. Aiming at this situation, an online detection method of toothbrush hair loss based on machine vision is proposed. Firstly, an image acquisition system is designed. Then, image graying, image sharpening and image matching are used to detect toothbrush hair loss in turn, and online detection software is designed. The experimental results show that this method can effectively detect the absence of toothbrush hair, and the recognition accuracy can reach more than 95%.
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Bao, N., Fang, H. (2020). Detection of Toothbrush Hair Loss Based on Machine Vision. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1074. Springer, Cham. https://doi.org/10.1007/978-3-030-32456-8_70
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DOI: https://doi.org/10.1007/978-3-030-32456-8_70
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