YOLOV9-CBM: An Improved Fire Detection Algorithm Based on YOLOV9
Regarding the current problems of false alarms and missed detections in fire detection, we propose a high-precision fire detection algorithm, YOLOV9-CBM (C3-SE, BiFPN, MPDIoU), by optimizing YOLOV9. Firstly, to tackle the shortage of both quality and quantity in the existing fire datasets, we collec...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10854439/ |
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| author | Xin Geng Xiao Han Xianghong Cao Yixuan Su Dongxue Shu |
| author_facet | Xin Geng Xiao Han Xianghong Cao Yixuan Su Dongxue Shu |
| author_sort | Xin Geng |
| collection | DOAJ |
| description | Regarding the current problems of false alarms and missed detections in fire detection, we propose a high-precision fire detection algorithm, YOLOV9-CBM (C3-SE, BiFPN, MPDIoU), by optimizing YOLOV9. Firstly, to tackle the shortage of both quality and quantity in the existing fire datasets, we collected 2,000 fire and smoke images to establish a dataset named CBM-Fire. Secondly, the RepNCSPELAN4 module of the YOLOv9 backbone was replaced with the C3 module containing SE Attention to improve detection efficiency while guaranteeing accuracy. Besides, we transformed the multi-scale fusion network PANet in the baseline algorithm into a bidirectional feature network pyramid BiFPN to facilitate the bidirectional flow of features, enabling the algorithm to fuse information at different scales more effectively. Finally, instead of CIoU losses, we adopted MPDIoU losses in bounding box regression, which improved the accuracy of model regression and classification. Experimental results indicate that compared with YOLOV9, the recall rate of YOLOV9-CBM has increased by 7.6% and the mAP has risen by 3.8%. The revised model demonstrates good generalization performance and robustness. Code and dataset are at <uri>https://github.com/GengHan-123/yolov9-cbm.git</uri>. |
| format | Article |
| id | doaj-art-9c7d5a83223949a787a50a4295a95b32 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-9c7d5a83223949a787a50a4295a95b322025-01-31T23:05:02ZengIEEEIEEE Access2169-35362025-01-0113196121962310.1109/ACCESS.2025.353478210854439YOLOV9-CBM: An Improved Fire Detection Algorithm Based on YOLOV9Xin Geng0https://orcid.org/0009-0000-7269-1830Xiao Han1Xianghong Cao2https://orcid.org/0009-0008-1667-9077Yixuan Su3Dongxue Shu4College of Building Environment Engineering, Zhengzhou University of Light Industry, Zhengzhou, Henan, ChinaCollege of Building Environment Engineering, Zhengzhou University of Light Industry, Zhengzhou, Henan, ChinaCollege of Building Environment Engineering, Zhengzhou University of Light Industry, Zhengzhou, Henan, ChinaSchool of Mechatronic Engineering and Automation, Shanghai University, Shanghai, ChinaCollege of Building Environment Engineering, Zhengzhou University of Light Industry, Zhengzhou, Henan, ChinaRegarding the current problems of false alarms and missed detections in fire detection, we propose a high-precision fire detection algorithm, YOLOV9-CBM (C3-SE, BiFPN, MPDIoU), by optimizing YOLOV9. Firstly, to tackle the shortage of both quality and quantity in the existing fire datasets, we collected 2,000 fire and smoke images to establish a dataset named CBM-Fire. Secondly, the RepNCSPELAN4 module of the YOLOv9 backbone was replaced with the C3 module containing SE Attention to improve detection efficiency while guaranteeing accuracy. Besides, we transformed the multi-scale fusion network PANet in the baseline algorithm into a bidirectional feature network pyramid BiFPN to facilitate the bidirectional flow of features, enabling the algorithm to fuse information at different scales more effectively. Finally, instead of CIoU losses, we adopted MPDIoU losses in bounding box regression, which improved the accuracy of model regression and classification. Experimental results indicate that compared with YOLOV9, the recall rate of YOLOV9-CBM has increased by 7.6% and the mAP has risen by 3.8%. The revised model demonstrates good generalization performance and robustness. Code and dataset are at <uri>https://github.com/GengHan-123/yolov9-cbm.git</uri>.https://ieeexplore.ieee.org/document/10854439/BiFPNC3fire detectionMPDIoUSEYOLOV9 |
| spellingShingle | Xin Geng Xiao Han Xianghong Cao Yixuan Su Dongxue Shu YOLOV9-CBM: An Improved Fire Detection Algorithm Based on YOLOV9 IEEE Access BiFPN C3 fire detection MPDIoU SE YOLOV9 |
| title | YOLOV9-CBM: An Improved Fire Detection Algorithm Based on YOLOV9 |
| title_full | YOLOV9-CBM: An Improved Fire Detection Algorithm Based on YOLOV9 |
| title_fullStr | YOLOV9-CBM: An Improved Fire Detection Algorithm Based on YOLOV9 |
| title_full_unstemmed | YOLOV9-CBM: An Improved Fire Detection Algorithm Based on YOLOV9 |
| title_short | YOLOV9-CBM: An Improved Fire Detection Algorithm Based on YOLOV9 |
| title_sort | yolov9 cbm an improved fire detection algorithm based on yolov9 |
| topic | BiFPN C3 fire detection MPDIoU SE YOLOV9 |
| url | https://ieeexplore.ieee.org/document/10854439/ |
| work_keys_str_mv | AT xingeng yolov9cbmanimprovedfiredetectionalgorithmbasedonyolov9 AT xiaohan yolov9cbmanimprovedfiredetectionalgorithmbasedonyolov9 AT xianghongcao yolov9cbmanimprovedfiredetectionalgorithmbasedonyolov9 AT yixuansu yolov9cbmanimprovedfiredetectionalgorithmbasedonyolov9 AT dongxueshu yolov9cbmanimprovedfiredetectionalgorithmbasedonyolov9 |