A multi-object detection method for building fire warnings through artificial intelligence generated content
Abstract Timely fire warnings are crucial for minimizing casualties during building fires. In this paper, a multi-object detection method through artificial intelligence generated content (AIGC) is proposed to improve building fire warning capability. First, an AIGC workflow of dataset construction...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-02865-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849705051434516480 |
|---|---|
| author | Jingwei Fu Zhen Xu Qingrui Yue Jiarui Lin Ning Zhang Yujie Zhao Donglian Gu |
| author_facet | Jingwei Fu Zhen Xu Qingrui Yue Jiarui Lin Ning Zhang Yujie Zhao Donglian Gu |
| author_sort | Jingwei Fu |
| collection | DOAJ |
| description | Abstract Timely fire warnings are crucial for minimizing casualties during building fires. In this paper, a multi-object detection method through artificial intelligence generated content (AIGC) is proposed to improve building fire warning capability. First, an AIGC workflow of dataset construction on building fire images is designed, to overcome the limitation due to a serious lack of real building fire images. Validation experiments demonstrate that the detection accuracy of the model trained on the AIGC dataset is only 1.6% lower than that of the model trained on the real image dataset. Subsequently, a multi-object detection model is developed to enhance its feature capture capability, by incorporating the MLCA mechanism into its backbone and replacing the feature fusion layer in its neck. The developed model can detect the flame and smoke of building fires with an accuracy of 95.7%. Finally, the case study involving three real fire incidents demonstrates that the proposed method can detect fires within 2s since the fire starting, which achieves an improvement of at least 6.5 times in the fire warning efficiency compared to the traditional fire alarms. Therefore, the proposed method can deliver timely fire warnings for the evacuation and rescue efforts during building fires. |
| format | Article |
| id | doaj-art-c35f0c56cc1945cbb7ed7f227706916d |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c35f0c56cc1945cbb7ed7f227706916d2025-08-20T03:16:34ZengNature PortfolioScientific Reports2045-23222025-05-0115111310.1038/s41598-025-02865-4A multi-object detection method for building fire warnings through artificial intelligence generated contentJingwei Fu0Zhen Xu1Qingrui Yue2Jiarui Lin3Ning Zhang4Yujie Zhao5Donglian Gu6Research Institute of Urbanization and Urban Safety, School of Future Cities, University of Science and Technology BeijingResearch Institute of Urbanization and Urban Safety, School of Future Cities, University of Science and Technology BeijingResearch Institute of Urbanization and Urban Safety, School of Future Cities, University of Science and Technology BeijingDepartment of Civil Engineering, Tsinghua UniversityResearch Institute of Urbanization and Urban Safety, School of Future Cities, University of Science and Technology BeijingResearch Institute of Urbanization and Urban Safety, School of Future Cities, University of Science and Technology BeijingResearch Institute of Urbanization and Urban Safety, School of Future Cities, University of Science and Technology BeijingAbstract Timely fire warnings are crucial for minimizing casualties during building fires. In this paper, a multi-object detection method through artificial intelligence generated content (AIGC) is proposed to improve building fire warning capability. First, an AIGC workflow of dataset construction on building fire images is designed, to overcome the limitation due to a serious lack of real building fire images. Validation experiments demonstrate that the detection accuracy of the model trained on the AIGC dataset is only 1.6% lower than that of the model trained on the real image dataset. Subsequently, a multi-object detection model is developed to enhance its feature capture capability, by incorporating the MLCA mechanism into its backbone and replacing the feature fusion layer in its neck. The developed model can detect the flame and smoke of building fires with an accuracy of 95.7%. Finally, the case study involving three real fire incidents demonstrates that the proposed method can detect fires within 2s since the fire starting, which achieves an improvement of at least 6.5 times in the fire warning efficiency compared to the traditional fire alarms. Therefore, the proposed method can deliver timely fire warnings for the evacuation and rescue efforts during building fires.https://doi.org/10.1038/s41598-025-02865-4 |
| spellingShingle | Jingwei Fu Zhen Xu Qingrui Yue Jiarui Lin Ning Zhang Yujie Zhao Donglian Gu A multi-object detection method for building fire warnings through artificial intelligence generated content Scientific Reports |
| title | A multi-object detection method for building fire warnings through artificial intelligence generated content |
| title_full | A multi-object detection method for building fire warnings through artificial intelligence generated content |
| title_fullStr | A multi-object detection method for building fire warnings through artificial intelligence generated content |
| title_full_unstemmed | A multi-object detection method for building fire warnings through artificial intelligence generated content |
| title_short | A multi-object detection method for building fire warnings through artificial intelligence generated content |
| title_sort | multi object detection method for building fire warnings through artificial intelligence generated content |
| url | https://doi.org/10.1038/s41598-025-02865-4 |
| work_keys_str_mv | AT jingweifu amultiobjectdetectionmethodforbuildingfirewarningsthroughartificialintelligencegeneratedcontent AT zhenxu amultiobjectdetectionmethodforbuildingfirewarningsthroughartificialintelligencegeneratedcontent AT qingruiyue amultiobjectdetectionmethodforbuildingfirewarningsthroughartificialintelligencegeneratedcontent AT jiaruilin amultiobjectdetectionmethodforbuildingfirewarningsthroughartificialintelligencegeneratedcontent AT ningzhang amultiobjectdetectionmethodforbuildingfirewarningsthroughartificialintelligencegeneratedcontent AT yujiezhao amultiobjectdetectionmethodforbuildingfirewarningsthroughartificialintelligencegeneratedcontent AT dongliangu amultiobjectdetectionmethodforbuildingfirewarningsthroughartificialintelligencegeneratedcontent AT jingweifu multiobjectdetectionmethodforbuildingfirewarningsthroughartificialintelligencegeneratedcontent AT zhenxu multiobjectdetectionmethodforbuildingfirewarningsthroughartificialintelligencegeneratedcontent AT qingruiyue multiobjectdetectionmethodforbuildingfirewarningsthroughartificialintelligencegeneratedcontent AT jiaruilin multiobjectdetectionmethodforbuildingfirewarningsthroughartificialintelligencegeneratedcontent AT ningzhang multiobjectdetectionmethodforbuildingfirewarningsthroughartificialintelligencegeneratedcontent AT yujiezhao multiobjectdetectionmethodforbuildingfirewarningsthroughartificialintelligencegeneratedcontent AT dongliangu multiobjectdetectionmethodforbuildingfirewarningsthroughartificialintelligencegeneratedcontent |