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

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 and Kevin Fong-Rey Liu ()
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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
Meng-Jey Youh: Department of Mechanical Engineering, Ming Chi University of Technology, New Taipei City 243303, Taiwan
Yen-Ting Chou: Department of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei City 243303, Taiwan
Chih-Hsiang Kang: Center of Artificial Intelligent and Data Science, Ming Chi University of Technology, New Taipei City 243303, Taiwan
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

Mathematics, 2023, vol. 11, issue 17, 1-18

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.

Keywords: smoke detection; deep domain adaptation; automatic labeling; motion detection; convolutional neural networks (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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