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U-Net-Based Foreign Object Detection Method Using Effective Image Acquisition System: A Case of Almond and Green Onion Flake Food Process

Author

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  • Guk-Jin Son

    (ICT Research Institute, DGIST, Daegu 42988, Korea
    Department of Artificial Intelligence, Kyungpook National University, Daegu 41566, Korea)

  • Dong-Hoon Kwak

    (ICT Research Institute, DGIST, Daegu 42988, Korea)

  • Mi-Kyung Park

    (School of Food Science and Biotechnology, Kyungpook National University, Daegu 41566, Korea)

  • Young-Duk Kim

    (ICT Research Institute, DGIST, Daegu 42988, Korea)

  • Hee-Chul Jung

    (Department of Artificial Intelligence, Kyungpook National University, Daegu 41566, Korea)

Abstract

Supervised deep learning-based foreign object detection algorithms are tedious, costly, and time-consuming because they usually require a large number of training datasets and annotations. These disadvantages make them frequently unsuitable for food quality evaluation and food manufacturing processes. However, the deep learning-based foreign object detection algorithm is an effective method to overcome the disadvantages of conventional foreign object detection methods mainly used in food inspection. For example, color sorter machines cannot detect foreign objects with a color similar to food, and the performance is easily degraded by changes in illuminance. Therefore, to detect foreign objects, we use a deep learning-based foreign object detection algorithm (model). In this paper, we present a synthetic method to efficiently acquire a training dataset of deep learning that can be used for food quality evaluation and food manufacturing processes. Moreover, we perform data augmentation using color jitter on a synthetic dataset and show that this approach significantly improves the illumination invariance features of the model trained on synthetic datasets. The F1-score of the model that trained the synthetic dataset of almonds at 360 lux illumination intensity achieved a performance of 0.82, similar to the F1-score of the model that trained the real dataset. Moreover, the F1-score of the model trained with the real dataset combined with the synthetic dataset achieved better performance than the model trained with the real dataset in the change of illumination. In addition, compared with the traditional method of using color sorter machines to detect foreign objects, the model trained on the synthetic dataset has obvious advantages in accuracy and efficiency. These results indicate that the synthetic dataset not only competes with the real dataset, but they also complement each other.

Suggested Citation

  • Guk-Jin Son & Dong-Hoon Kwak & Mi-Kyung Park & Young-Duk Kim & Hee-Chul Jung, 2021. "U-Net-Based Foreign Object Detection Method Using Effective Image Acquisition System: A Case of Almond and Green Onion Flake Food Process," Sustainability, MDPI, vol. 13(24), pages 1-20, December.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:24:p:13834-:d:702658
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    References listed on IDEAS

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    1. Sungyul Chang & Unseok Lee & Min Jeong Hong & Yeong Deuk Jo & Jin-Baek Kim, 2021. "Lettuce Growth Pattern Analysis Using U-Net Pre-Trained with Arabidopsis," Agriculture, MDPI, vol. 11(9), pages 1-8, September.
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    3. Sandeep Jagtap & Chintan Bhatt & Jaydeep Thik & Shahin Rahimifard, 2019. "Monitoring Potato Waste in Food Manufacturing Using Image Processing and Internet of Things Approach," Sustainability, MDPI, vol. 11(11), pages 1-12, June.
    4. Artur Janowski & Rafał Kaźmierczak & Cezary Kowalczyk & Jakub Szulwic, 2021. "Detecting Apples in the Wild: Potential for Harvest Quantity Estimation," Sustainability, MDPI, vol. 13(14), pages 1-15, July.
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