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Detection Method of Crushing Mouth Loose Material Blockage Based on SSD Algorithm

Author

Listed:
  • Jiang Yao

    (College of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China)

  • Zhiqiang Wang

    (Chinese Academy of Sciences Allwin Technology Co., Ltd., Shenyang 110179, China)

  • Chunhui Liu

    (Ansteel Group Guanbaoshan Mining Co., Ltd., Anshan 114044, China)

  • Guichen Huang

    (Ansteel Group Guanbaoshan Mining Co., Ltd., Anshan 114044, China)

  • Qingbo Yuan

    (Ansteel Group Guanbaoshan Mining Co., Ltd., Anshan 114044, China)

  • Kai Xu

    (Ansteel Group Guanbaoshan Mining Co., Ltd., Anshan 114044, China)

  • Wenhui Zhang

    (Ansteel Group Guanbaoshan Mining Co., Ltd., Anshan 114044, China)

Abstract

With the advancement of smart mines technology, unmanned and Shojinka have received widespread attention, among which unattended crushing station is one of the research directions. To realize unattended crushing station, first of all, it is necessary to detect loose material blockage at the crushing mouth. Based on deep learning (DL) and machine vision (MV) technology, an on-line detection method is studied to trace the blockage in a swift and accurate manner, so that the corresponding detection system can be designed accordingly. The charge coupled device (CCD) industrial camera set above the crushing mouth is used to collect images and input them to the edge computing equipment. The original Single Shot MultiBox Detector (SSD) preprocessing model is trained and optimized before it is combined with the MV technology to detect and then the MV technology is combined to detect whether the crushing mouth is covered. In Ansteel Group GUANBAOSHAN mine, the accuracy of recognition and detection system with human observation was examined for one month, and the tested accuracy is 95%. The experimental results show that the proposed method can detect the crushing mouth blockage in real time, which would solve the problem that the blockage can only be identified by human eyes in traditional method, and provides basic support for the unattended crushing station.

Suggested Citation

  • Jiang Yao & Zhiqiang Wang & Chunhui Liu & Guichen Huang & Qingbo Yuan & Kai Xu & Wenhui Zhang, 2022. "Detection Method of Crushing Mouth Loose Material Blockage Based on SSD Algorithm," Sustainability, MDPI, vol. 14(21), pages 1-16, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:14386-:d:961888
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    References listed on IDEAS

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    1. Poonam Pawar & Bharati Ainapure & Mamoon Rashid & Nazir Ahmad & Aziz Alotaibi & Sultan S. Alshamrani, 2022. "Deep Learning Approach for the Detection of Noise Type in Ancient Images," Sustainability, MDPI, vol. 14(18), pages 1-19, September.
    2. Mesfer Al Duhayyim & Heba G. Mohamed & Mohammed Aljebreen & Mohamed K. Nour & Abdullah Mohamed & Amgad Atta Abdelmageed & Ishfaq Yaseen & Gouse Pasha Mohammed, 2022. "Artificial Ecosystem-Based Optimization with an Improved Deep Learning Model for IoT-Assisted Sustainable Waste Management," Sustainability, MDPI, vol. 14(18), pages 1-17, September.
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    Cited by:

    1. Piotr Bortnowski & Horst Gondek & Robert Król & Daniela Marasova & Maksymilian Ozdoba, 2023. "Detection of Blockages of the Belt Conveyor Transfer Point Using an RGB Camera and CNN Autoencoder," Energies, MDPI, vol. 16(4), pages 1-18, February.

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