Abstract
Quality inspection is an essential technology in the glass product industry. Machine vision has shown more significant potential than manual inspection at present. However, the visual inspection of the bottle for defects remains a challenging task in a quality-controlled due to the difficulty in detecting some notable defects. To overcome the problem, we propose a surface defect detection framework based on the stripe light source. First, a novel method, DTST, determines the stripe type in the background with traditional image processing methods. Then, according to the result of DTST, the stripe type is divided into vertical stripes and horizontal stripes. For the former, a defect detection method based on DDVS that uses machine learning technology is proposed to detect cold mold defects precisely. A defect detection method named DDHS that uses deep learning technology is proposed to precisely detect wrinkled skin defects for the latter. The proposed framework is tested for data sets obtained by our designed vision system. The experimental results demonstrate that our framework achieves good performance.
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Acknowledgement
This study is supported by the key research project of the Ministry of Science and Technology (Grant No. 2018YFB1306802) the National Natural Science Foundation of China (Grant No. 51975344) and China Postdoctoral Science Foundation (Grant No. 2019M662591).
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Lu, J., Zhang, X., Li, C. (2021). An Surface Defect Detection Framework for Glass Bottle Body Based on the Stripe Light Source. In: Liu, XJ., Nie, Z., Yu, J., Xie, F., Song, R. (eds) Intelligent Robotics and Applications. ICIRA 2021. Lecture Notes in Computer Science(), vol 13014. Springer, Cham. https://doi.org/10.1007/978-3-030-89098-8_54
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DOI: https://doi.org/10.1007/978-3-030-89098-8_54
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