DG-YOLO: A Novel Efficient Early Fire Detection Algorithm Under Complex Scenarios.
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| Title: | DG-YOLO: A Novel Efficient Early Fire Detection Algorithm Under Complex Scenarios. |
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| Authors: | Jiang, Xuefeng1 (AUTHOR) 2022200172@aust.edu.cn, Xu, Liuquan2 (AUTHOR) liuquanxu@aust.edu.cn, Fang, Xianjin2 (AUTHOR) xjfang@aust.edu.cn |
| Source: | Fire Technology. Jul2025, Vol. 61 Issue 4, p2047-2071. 25p. |
| Subjects: | Detection algorithms, Optical interference, Feature extraction, Deep learning, Artificial intelligence, Fire detectors, False alarms |
| Abstract: | In reality, it is important to control fires in their early stages. However, the early stages of a fire are characterized by small flames with blurred edges. Additionally, the interference in complex scenarios involving occlusion, light interference, and fire-like objects leads to a high leakage rate and false detection rate of existing target detection methods in early fire detection. To address the above problems, this paper proposes a novel and efficient method for early fire detection in complex scenarios, called DG-YOLO. Firstly, a deformable attention (DA) is introduced in the YOLOv8 backbone. Focusing on small fire features, it enhances the anti-interference ability of the model in complex scenes. Secondly, the addition of a lightweight feature extraction module (GSC2f) gives the model a rich gradient flow to capture early flame edge features, thus enabling effective multi-scale feature fusion. Finally, to address the limitations of small early flames and blurred edges, we introduce a small-target detector. It effectively captures the shape and texture information of early fires in complex scenes and reduces the leakage rate and false alarm rate. Comprehensive experiments have been conducted on a dataset of real-life scenarios. The results of the study show that the F1 score and mAP50 metrics are improved by an astonishing 9.77% and 10.7%, respectively. The leakage rate and false alarm rate are effectively reduced. Meanwhile, comparison experiments show that DG-YOLO surpasses the current advanced technology. The efficiency of the model for early fire detection in complex scenarios is demonstrated. [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 |
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| Items | – Name: Title Label: Title Group: Ti Data: DG-YOLO: A Novel Efficient Early Fire Detection Algorithm Under Complex Scenarios. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Jiang%2C+Xuefeng%22">Jiang, Xuefeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 2022200172@aust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Xu%2C+Liuquan%22">Xu, Liuquan</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> liuquanxu@aust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Fang%2C+Xianjin%22">Fang, Xianjin</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> xjfang@aust.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Fire+Technology%22">Fire Technology</searchLink>. Jul2025, Vol. 61 Issue 4, p2047-2071. 25p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Detection+algorithms%22">Detection algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Optical+interference%22">Optical interference</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Fire+detectors%22">Fire detectors</searchLink><br /><searchLink fieldCode="DE" term="%22False+alarms%22">False alarms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In reality, it is important to control fires in their early stages. However, the early stages of a fire are characterized by small flames with blurred edges. Additionally, the interference in complex scenarios involving occlusion, light interference, and fire-like objects leads to a high leakage rate and false detection rate of existing target detection methods in early fire detection. To address the above problems, this paper proposes a novel and efficient method for early fire detection in complex scenarios, called DG-YOLO. Firstly, a deformable attention (DA) is introduced in the YOLOv8 backbone. Focusing on small fire features, it enhances the anti-interference ability of the model in complex scenes. Secondly, the addition of a lightweight feature extraction module (GSC2f) gives the model a rich gradient flow to capture early flame edge features, thus enabling effective multi-scale feature fusion. Finally, to address the limitations of small early flames and blurred edges, we introduce a small-target detector. It effectively captures the shape and texture information of early fires in complex scenes and reduces the leakage rate and false alarm rate. Comprehensive experiments have been conducted on a dataset of real-life scenarios. The results of the study show that the F1 score and mAP50 metrics are improved by an astonishing 9.77% and 10.7%, respectively. The leakage rate and false alarm rate are effectively reduced. Meanwhile, comparison experiments show that DG-YOLO surpasses the current advanced technology. The efficiency of the model for early fire detection in complex scenarios is demonstrated. [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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10694-024-01672-z Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 2047 Subjects: – SubjectFull: Detection algorithms Type: general – SubjectFull: Optical interference Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Fire detectors Type: general – SubjectFull: False alarms Type: general Titles: – TitleFull: DG-YOLO: A Novel Efficient Early Fire Detection Algorithm Under Complex Scenarios. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Jiang, Xuefeng – PersonEntity: Name: NameFull: Xu, Liuquan – PersonEntity: Name: NameFull: Fang, Xianjin 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 |
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