Journal of Terahertz Science and Electronic Information Technology , Volume. 22, Issue 7, 776(2024)

Raspberry Pi flame recognition system based on improved YOLOv5

DENG Li*, XIE Shuangshuang, ZHU Bo, WU Dandan, and LIU Quanyi
Author Affiliations
  • [in Chinese]
  • show less

    Fire disaster can cause great harm to the safety of people and property, and how to detect flame intelligently and efficiently is of great significance. In order to achieve accurate flame recognition under high space conditions, an infrared camera with two degrees of freedom that can detect environmental conditions in all directions is designed, and the target detection algorithm YOLOv5 is improved combined with deep learning. The K-Means clustering algorithm is employed to obtain nine width and height dimensions of clustering center by matching and replace the original network anchor parameters. Considering the relative proportion of the target frame, the loss function is optimized and applied to the Raspberry Pi to achieve flame recognition. The test results show that it takes 2.9 s for the improved YOLOv5 algorithm to detect a single sheet on the Raspberry Pi, which is less than that for the original YOLOv5 algorithm by 78%. The accuracy of the system is 100%, and the confidence of identifying the target frame is above 0.9. The proposed system can identify the flame fast and accurately.

    Tools

    Get Citation

    Copy Citation Text

    DENG Li, XIE Shuangshuang, ZHU Bo, WU Dandan, LIU Quanyi. Raspberry Pi flame recognition system based on improved YOLOv5[J]. Journal of Terahertz Science and Electronic Information Technology , 2024, 22(7): 776

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category:

    Received: Aug. 24, 2022

    Accepted: --

    Published Online: Aug. 22, 2024

    The Author Email: Li DENG (bitdengli@163.com)

    DOI:10.11805/tkyda2022156

    Topics