FCMI-YOLO: An efficient deep learning-based algorithm for real-time fire detection on edge devices.
The rapid development of Internet of Things (IoT) technology and deep learning has propelled the deployment of vision-based fire detection algorithms on edge devices, significantly exacerbating the trade-off between accuracy and inference speed under hardware resource constraints. To address this is...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0329555 |
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| _version_ | 1849228158310547456 |
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| author | Junjie Lu Yuchen Zheng Liwei Guan Bing Lin Wenzao Shi Junyan Zhang Yunping Wu |
| author_facet | Junjie Lu Yuchen Zheng Liwei Guan Bing Lin Wenzao Shi Junyan Zhang Yunping Wu |
| author_sort | Junjie Lu |
| collection | DOAJ |
| description | The rapid development of Internet of Things (IoT) technology and deep learning has propelled the deployment of vision-based fire detection algorithms on edge devices, significantly exacerbating the trade-off between accuracy and inference speed under hardware resource constraints. To address this issue, this paper proposes FCMI-YOLO, a real-time fire detection algorithm optimized for edge devices. Firstly, the FasterNext module is proposed to reduce computational cost and enhance detection precision through lightweight design. Secondly, the Cross-Scale Feature Fusion Module (CCFM) and the Mixed Local Channel Attention (MLCA) mechanism are incorporated into the neck network to improve detection performance for small fire targets and reduce resource consumption. Finally, the Inner-DIoU loss function is proposed to optimize bounding box regression. Experimental results on a custom fire dataset demonstrate that FCMI-YOLO increases mAP@50 by 1.5%, reduces parameters by 40%, and lowers GFLOPs to 28.9% of YOLOv5s, demonstrating its practical value for real-time fire detection in edge scenarios with limited computational resources. The core code and dataset are available at https://github.com/ JunJieLu20230823/code.git. |
| format | Article |
| id | doaj-art-741ca74fb7ee49dcb26819b1a7ee6072 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-741ca74fb7ee49dcb26819b1a7ee60722025-08-23T05:32:01ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01208e032955510.1371/journal.pone.0329555FCMI-YOLO: An efficient deep learning-based algorithm for real-time fire detection on edge devices.Junjie LuYuchen ZhengLiwei GuanBing LinWenzao ShiJunyan ZhangYunping WuThe rapid development of Internet of Things (IoT) technology and deep learning has propelled the deployment of vision-based fire detection algorithms on edge devices, significantly exacerbating the trade-off between accuracy and inference speed under hardware resource constraints. To address this issue, this paper proposes FCMI-YOLO, a real-time fire detection algorithm optimized for edge devices. Firstly, the FasterNext module is proposed to reduce computational cost and enhance detection precision through lightweight design. Secondly, the Cross-Scale Feature Fusion Module (CCFM) and the Mixed Local Channel Attention (MLCA) mechanism are incorporated into the neck network to improve detection performance for small fire targets and reduce resource consumption. Finally, the Inner-DIoU loss function is proposed to optimize bounding box regression. Experimental results on a custom fire dataset demonstrate that FCMI-YOLO increases mAP@50 by 1.5%, reduces parameters by 40%, and lowers GFLOPs to 28.9% of YOLOv5s, demonstrating its practical value for real-time fire detection in edge scenarios with limited computational resources. The core code and dataset are available at https://github.com/ JunJieLu20230823/code.git.https://doi.org/10.1371/journal.pone.0329555 |
| spellingShingle | Junjie Lu Yuchen Zheng Liwei Guan Bing Lin Wenzao Shi Junyan Zhang Yunping Wu FCMI-YOLO: An efficient deep learning-based algorithm for real-time fire detection on edge devices. PLoS ONE |
| title | FCMI-YOLO: An efficient deep learning-based algorithm for real-time fire detection on edge devices. |
| title_full | FCMI-YOLO: An efficient deep learning-based algorithm for real-time fire detection on edge devices. |
| title_fullStr | FCMI-YOLO: An efficient deep learning-based algorithm for real-time fire detection on edge devices. |
| title_full_unstemmed | FCMI-YOLO: An efficient deep learning-based algorithm for real-time fire detection on edge devices. |
| title_short | FCMI-YOLO: An efficient deep learning-based algorithm for real-time fire detection on edge devices. |
| title_sort | fcmi yolo an efficient deep learning based algorithm for real time fire detection on edge devices |
| url | https://doi.org/10.1371/journal.pone.0329555 |
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