IJAT Vol.16 No.6 pp. 766-772
doi: 10.20965/ijat.2022.p0766


Visual Identification-Based Spark Recognition System

Tianhao Cheng*, Hao Hu**, Hitoshi Kobayashi**, and Hiroshi Onoda*,†

*Graduate School of Environment and Energy Engineering, Waseda University
513 Wasedatsurumakicho, Shinjuku-ku, Tokyo 162-0041, Japan

Corresponding author

**EII, Inc., Tokyo, Japan

April 27, 2022
August 5, 2022
November 5, 2022
visual recognition, fire-detection system, lithium-ion battery, early detection and extinguishing, recycling facility

With the development of artificial intelligence, image recognition has seen wider adoption. Here, a novel paradigm image recognition system is proposed for detection of fires owing to the compression of lithium-ion batteries at recycling facilities. The proposed system uses deep learning method. The SparkEye system is proposed, focusing on the early detection of fires as sparks, and is combined with a sprinkler system, to minimize fire-related losses at affected facilities. Approximately 30,000 images (resolution, 800 × 600 pixels) were used for training the system to >90% detection accuracy. To fulfil the demand for dust control at recycling facilities, air and frame camera protection methods were incorporated into the system. Based on the test data and realistic workplace feedback, the best placements of the SparkEye fire detectors were crushers, conveyors, and garbage pits.

Cite this article as:
T. Cheng, H. Hu, H. Kobayashi, and H. Onoda, “Visual Identification-Based Spark Recognition System,” Int. J. Automation Technol., Vol.16, No.6, pp. 766-772, 2022.
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Last updated on Dec. 01, 2022