View in EDS HTML Full Text PDF Full Text

Fire Video Intelligent Monitoring Method Based on Moving Target Enhancement and PRV-YOLO Network.

Saved in:
Bibliographic Details
Title: Fire Video Intelligent Monitoring Method Based on Moving Target Enhancement and PRV-YOLO Network.
Authors: Wang, Hongyi1 (AUTHOR) wanghongyi@tiangong.edu.cn, Li, Anjing2 (AUTHOR), Yang, Yang2 (AUTHOR), Zhu, Xinjun1 (AUTHOR), Song, Limei2 (AUTHOR)
Source: Fire Technology. Jul2025, Vol. 61 Issue 4, p1463-1489. 27p.
Subjects: Video monitors, Image intensifiers, Artificial intelligence, Image processing, Smoke, Tunnel ventilation
Abstract: Different from objects with clear boundaries in target detection, the fire and smoke generated by fire are variable in shape and hard to be detected by traditional methods. To detect the fire and smoke accurately and timely, a fire identification method based on moving target enhancement and the PRV-YOLO network was proposed in this work. By considering the motion information of smoke and fire in the video data, a PCLAHE-KNN moving target enhancement algorithm is designed to roughly locate the target in the pre-processing stage. In the recognition stage, the PRV-YOLO network is developed for smoke and fire detection. For PRV-YOLO network, CSPResNeXt module is introduced in the backbone position and the VoVGSCSP module is used in the head position, which improves the detection speed and reduces the computation load of the model. Meanwhile, the priority boundary frame loss function PIoU is proposed to improve the regression speed and the accuracy of the detection model. The experimental results have shown that the proposed method has advantages in fire video monitoring, especially in terms of sensitivity to smoke in the early stages of a fire. [ABSTRACT FROM AUTHOR]
Copyright of Fire Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 185648727
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Fire Video Intelligent Monitoring Method Based on Moving Target Enhancement and PRV-YOLO Network.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Hongyi%22">Wang, Hongyi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> wanghongyi@tiangong.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Anjing%22">Li, Anjing</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Yang%22">Yang, Yang</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhu%2C+Xinjun%22">Zhu, Xinjun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Song%2C+Limei%22">Song, Limei</searchLink><relatesTo>2</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Fire+Technology%22">Fire Technology</searchLink>. Jul2025, Vol. 61 Issue 4, p1463-1489. 27p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Video+monitors%22">Video monitors</searchLink><br /><searchLink fieldCode="DE" term="%22Image+intensifiers%22">Image intensifiers</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Smoke%22">Smoke</searchLink><br /><searchLink fieldCode="DE" term="%22Tunnel+ventilation%22">Tunnel ventilation</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Different from objects with clear boundaries in target detection, the fire and smoke generated by fire are variable in shape and hard to be detected by traditional methods. To detect the fire and smoke accurately and timely, a fire identification method based on moving target enhancement and the PRV-YOLO network was proposed in this work. By considering the motion information of smoke and fire in the video data, a PCLAHE-KNN moving target enhancement algorithm is designed to roughly locate the target in the pre-processing stage. In the recognition stage, the PRV-YOLO network is developed for smoke and fire detection. For PRV-YOLO network, CSPResNeXt module is introduced in the backbone position and the VoVGSCSP module is used in the head position, which improves the detection speed and reduces the computation load of the model. Meanwhile, the priority boundary frame loss function PIoU is proposed to improve the regression speed and the accuracy of the detection model. The experimental results have shown that the proposed method has advantages in fire video monitoring, especially in terms of sensitivity to smoke in the early stages of a fire. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Fire Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=185648727
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s10694-024-01650-5
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 27
        StartPage: 1463
    Subjects:
      – SubjectFull: Video monitors
        Type: general
      – SubjectFull: Image intensifiers
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Image processing
        Type: general
      – SubjectFull: Smoke
        Type: general
      – SubjectFull: Tunnel ventilation
        Type: general
    Titles:
      – TitleFull: Fire Video Intelligent Monitoring Method Based on Moving Target Enhancement and PRV-YOLO Network.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Wang, Hongyi
      – PersonEntity:
          Name:
            NameFull: Li, Anjing
      – PersonEntity:
          Name:
            NameFull: Yang, Yang
      – PersonEntity:
          Name:
            NameFull: Zhu, Xinjun
      – PersonEntity:
          Name:
            NameFull: Song, Limei
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 07
              Text: Jul2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 00152684
          Numbering:
            – Type: volume
              Value: 61
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: Fire Technology
              Type: main
ResultId 1