IDEAS home Printed from https://ideas.repec.org/a/caa/jnljfs/v65y2019i4id82-2018-jfs.html
   My bibliography  Save this article

Early smoke detection of forest fires based on SVM image segmentation

Author

Listed:
  • Ding Xiong
  • Lu Yan

    (School of Electrical and Information Engineering, Hunan International Economics University, Changsha, China)

Abstract

A smoke detection method is proposed in single-frame video sequence images for forest fire detection in large space and complex scenes. A new superpixel merging algorithm is further studied to improve the existing horizon detection algorithm. This method performs Simple Linear Iterative Clustering (SLIC) superpixel segmentation on the image, and the over-segmentation problem is solved with a new superpixel merging algorithm. The improved sky horizon line segmentation algorithm is used to eliminate the interference of clouds in the sky for smoke detection. According to the spectral features, the superpixel blocks are classified by support vector machine (SVM). The experimental results show that the superpixel merging algorithm is efficient and simple, and easy to program. The smoke detection technology based on image segmentation can eliminate the interference of noise such as clouds and fog on smoke detection. The accuracy of smoke detection is 77% in a forest scene, it can be used as an auxiliary means of monitoring forest fires. A new attempt is given for forest fire warning and automatic detection.

Suggested Citation

  • Ding Xiong & Lu Yan, 2019. "Early smoke detection of forest fires based on SVM image segmentation," Journal of Forest Science, Czech Academy of Agricultural Sciences, vol. 65(4), pages 150-159.
    • Handle: RePEc:caa:jnljfs:v:65:y:2019:i:4:id:82-2018-jfs
    DOI: 10.17221/82/2018-JFS
    as

    Download full text from publisher

    File URL: http://jfs.agriculturejournals.cz/doi/10.17221/82/2018-JFS.html
    Download Restriction: free of charge
    File URL: http://jfs.agriculturejournals.cz/doi/10.17221/82/2018-JFS.pdf
    Download Restriction: free of charge
    File URL: https://libkey.io/10.17221/82/2018-JFS?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Haitao Wang & Yanli Chen, 2019. "A smoke image segmentation algorithm based on rough set and region growing," Journal of Forest Science, Czech Academy of Agricultural Sciences, vol. 65(8), pages 321-329.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:caa:jnljfs:v:65:y:2019:i:4:id:82-2018-jfs. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ivo Andrle (email available below). General contact details of provider: https://www.cazv.cz/en/home/ .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.