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Real-Time Video Smoke Detection Based on Deep Domain Adaptation for Injection Molding Machines

Author

Listed:
  • Ssu-Han Chen

    (Department of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei City 243303, Taiwan)

  • Jer-Huan Jang

    (Department of Mechanical Engineering, Ming Chi University of Technology, New Taipei City 243303, Taiwan
    These authors contributed equally to this work.)

  • Meng-Jey Youh

    (Department of Mechanical Engineering, Ming Chi University of Technology, New Taipei City 243303, Taiwan
    These authors contributed equally to this work.)

  • Yen-Ting Chou

    (Department of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei City 243303, Taiwan
    These authors contributed equally to this work.)

  • Chih-Hsiang Kang

    (Center of Artificial Intelligent and Data Science, Ming Chi University of Technology, New Taipei City 243303, Taiwan
    These authors contributed equally to this work.)

  • Chang-Yen Wu

    (1st Petrochemicals Division, Formosa Chemicals & Fibre Corporation, Taipei City 105076, Taiwan)

  • Chih-Ming Chen

    (1st Petrochemicals Division, Formosa Chemicals & Fibre Corporation, Taipei City 105076, Taiwan)

  • Jiun-Shiung Lin

    (Department of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei City 243303, Taiwan)

  • Jin-Kwan Lin

    (Department of Business and Management, Ming Chi University of Technology, New Taipei City 243303, Taiwan)

  • Kevin Fong-Rey Liu

    (Department of Safety, Health and Environmental Engineering, Ming Chi University of Technology, New Taipei City 243303, Taiwan)

Abstract

Leakage with smoke is often accompanied by fire and explosion hazards. Detecting smoke helps gain time for crisis management. This study aims to address this issue by establishing a video smoke detection system, based on a convolutional neural network (CNN), with the help of smoke synthesis, auto-annotation, and an attention mechanism by fusing gray histogram image information. Additionally, the study incorporates the domain adversarial training of neural networks (DANN) to investigate the effect of domain shifts when adapting the smoke detection model from one injection molding machine to another on-site. It achieves the function of domain confusion without requiring labeling, as well as the automatic extraction of domain features and automatic adversarial training, using target domain data. Compared to deep domain confusion (DDC), naïve DANN, and the domain separation network (DSN), the proposed method achieves the highest accuracy rates of 93.17% and 91.35% in both scenarios. Furthermore, the experiment employs t-distributed stochastic neighbor embedding (t-SNE) to facilitate fast training and smoke detection between machines by leveraging domain adaption features.

Suggested Citation

  • Ssu-Han Chen & Jer-Huan Jang & Meng-Jey Youh & Yen-Ting Chou & Chih-Hsiang Kang & Chang-Yen Wu & Chih-Ming Chen & Jiun-Shiung Lin & Jin-Kwan Lin & Kevin Fong-Rey Liu, 2023. "Real-Time Video Smoke Detection Based on Deep Domain Adaptation for Injection Molding Machines," Mathematics, MDPI, vol. 11(17), pages 1-18, August.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:17:p:3728-:d:1228858
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    References listed on IDEAS

    as
    1. Zhong Wang & Lei Wu & Tong Li & Peibei Shi, 2022. "A Smoke Detection Model Based on Improved YOLOv5," Mathematics, MDPI, vol. 10(7), pages 1-13, April.
    2. Shanni Li & Zhensheng Yang & Huabei Nie & Xiao Chen, 2022. "Corn Disease Detection Based on an Improved YOLOX-Tiny Network Model," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 16(1), pages 1-8, January.
    3. Alessio Gagliardi & Sergio Saponara, 2020. "AdViSED: Advanced Video SmokE Detection for Real-Time Measurements in Antifire Indoor and Outdoor Systems," Energies, MDPI, vol. 13(8), pages 1-18, April.
    Full references (including those not matched with items on IDEAS)

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