FB-YOLOv8s: A fire detection algorithm based on YOLOv8s
The significance of fire detection lies in protecting public safety and safeguarding the lives and property of people. However, there exist some problems in traditional detection algorithms of fire, such as low accuracy, high miss rate, and low detection rate of small targets. To effectively solve t...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co. Ltd.
2025-01-01
|
| Series: | Cognitive Robotics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667241325000163 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850117739733057536 |
|---|---|
| author | Yuhang Liu Chunjuan Bo Chong Feng |
| author_facet | Yuhang Liu Chunjuan Bo Chong Feng |
| author_sort | Yuhang Liu |
| collection | DOAJ |
| description | The significance of fire detection lies in protecting public safety and safeguarding the lives and property of people. However, there exist some problems in traditional detection algorithms of fire, such as low accuracy, high miss rate, and low detection rate of small targets. To effectively solve these issues, a fire detection algorithm based on YOLOv8s is introduced in this paper, called FB-YOLOv8s. First, the FasterNet lightweight network is introduced into the YOLOv8s network, merging the FasterNet Block structure of FasterNet with the original C2f modules to reduce the number of model parameters. Second, the Bi-directional Feature Pyramid Network (BiFPN) is incorporated to replace the Path Aggregation Network (PANet) in the neck network to enhance the model’s feature fusion capability. Finally, we adopt the WIoUv3 loss function to optimize the training process and improve detection accuracy. The experimental results demonstrate that compared to the original algorithm, the mAP0.5 of FB-YOLOv8s increases by 2.0 %, and the number of parameters decreases by 25.23 %. This method has better detection performance for fire targets. |
| format | Article |
| id | doaj-art-2dacc28da47544c8985fa0efd0253676 |
| institution | OA Journals |
| issn | 2667-2413 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Cognitive Robotics |
| spelling | doaj-art-2dacc28da47544c8985fa0efd02536762025-08-20T02:36:02ZengKeAi Communications Co. Ltd.Cognitive Robotics2667-24132025-01-01524024810.1016/j.cogr.2025.06.002FB-YOLOv8s: A fire detection algorithm based on YOLOv8sYuhang Liu0Chunjuan Bo1Chong Feng2School of Information and Communication Engineering, Dalian Minzu University, ChinaCorresponding author.; School of Information and Communication Engineering, Dalian Minzu University, ChinaSchool of Information and Communication Engineering, Dalian Minzu University, ChinaThe significance of fire detection lies in protecting public safety and safeguarding the lives and property of people. However, there exist some problems in traditional detection algorithms of fire, such as low accuracy, high miss rate, and low detection rate of small targets. To effectively solve these issues, a fire detection algorithm based on YOLOv8s is introduced in this paper, called FB-YOLOv8s. First, the FasterNet lightweight network is introduced into the YOLOv8s network, merging the FasterNet Block structure of FasterNet with the original C2f modules to reduce the number of model parameters. Second, the Bi-directional Feature Pyramid Network (BiFPN) is incorporated to replace the Path Aggregation Network (PANet) in the neck network to enhance the model’s feature fusion capability. Finally, we adopt the WIoUv3 loss function to optimize the training process and improve detection accuracy. The experimental results demonstrate that compared to the original algorithm, the mAP0.5 of FB-YOLOv8s increases by 2.0 %, and the number of parameters decreases by 25.23 %. This method has better detection performance for fire targets.http://www.sciencedirect.com/science/article/pii/S2667241325000163Fire detectionFB-YOLOv8sBiFPNWIoUv3 |
| spellingShingle | Yuhang Liu Chunjuan Bo Chong Feng FB-YOLOv8s: A fire detection algorithm based on YOLOv8s Cognitive Robotics Fire detection FB-YOLOv8s BiFPN WIoUv3 |
| title | FB-YOLOv8s: A fire detection algorithm based on YOLOv8s |
| title_full | FB-YOLOv8s: A fire detection algorithm based on YOLOv8s |
| title_fullStr | FB-YOLOv8s: A fire detection algorithm based on YOLOv8s |
| title_full_unstemmed | FB-YOLOv8s: A fire detection algorithm based on YOLOv8s |
| title_short | FB-YOLOv8s: A fire detection algorithm based on YOLOv8s |
| title_sort | fb yolov8s a fire detection algorithm based on yolov8s |
| topic | Fire detection FB-YOLOv8s BiFPN WIoUv3 |
| url | http://www.sciencedirect.com/science/article/pii/S2667241325000163 |
| work_keys_str_mv | AT yuhangliu fbyolov8safiredetectionalgorithmbasedonyolov8s AT chunjuanbo fbyolov8safiredetectionalgorithmbasedonyolov8s AT chongfeng fbyolov8safiredetectionalgorithmbasedonyolov8s |