Abstract
Nowadays, when high industrial productivity is connected with high quality and low product faults, it is common practice to use 100% product quality control. Since the quantities of products are high in mass production and inspection time must be as low as possible, the solution may be to use visual inspection of finished parts via camera systems and subsequent image processing using artificial intelligence. Recently, deep learning has shown itself to be the most appropriate and effective method for this purpose. The present article deals with the above-mentioned method of deep learning, and especially with its application when recognizing certain objects and elements during the visual product inspection.
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Acknowledgment
The research was supported by the Research Grant Agency under contract No. VEGA 1/0504/17 “Research and development of methods for multicriteria accuracy diagnostics of CNC machines”.
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Kuric, I., Kandera, M., Klarák, J., Ivanov, V., Więcek, D. (2020). Visual Product Inspection Based on Deep Learning Methods. In: Tonkonogyi, V., et al. Advanced Manufacturing Processes. InterPartner 2019. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-40724-7_15
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DOI: https://doi.org/10.1007/978-3-030-40724-7_15
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