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Faster R-CNN Based Fault Detection in Industrial Images

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Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices (IEA/AIE 2020)

Abstract

Industry 4.0 requires smart environment to find defects or faults in their products. A defective product in the market can impact negatively on the overall image of the industry. Thus, there is continuous struggle for industrial environment to reduce impulsive downtime, concert deprivation and safety risks. Defect detection in industrial products using the images is very hot topic in era of current research. Machine learning provides various solution but most of the time such solutions are not suitable for environment where product is on conveyor belt and traveling from one point to another. To detect fault using industrial images, we proposed a method which is based on Faster R-CNN which is suitable for smart environment as it can the product efficiently. We simulated our environment using python language and proposed model has almost 99% accuracy. To make our proposed scheme adaptable for the industry 4.0, we also developed an android application which make it easy to interact with the model and industry can train this model according to their needs. Android application is able to take pictures of defective product and feed it to model which improve accuracy and eventually reduces time identify defective product.

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Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2019R1F1A1060668).

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Correspondence to Seungmin Rho .

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Saeed, F., Paul, A., Rho, S. (2020). Faster R-CNN Based Fault Detection in Industrial Images. In: Fujita, H., Fournier-Viger, P., Ali, M., Sasaki, J. (eds) Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices. IEA/AIE 2020. Lecture Notes in Computer Science(), vol 12144. Springer, Cham. https://doi.org/10.1007/978-3-030-55789-8_25

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  • DOI: https://doi.org/10.1007/978-3-030-55789-8_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-55788-1

  • Online ISBN: 978-3-030-55789-8

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