Forest Fire Detection Algorithm Based on Improved YOLOv11n
To address issues in traditional forest fire detection models, such as large parameter sizes, slow detection speed, and unsuitability for resource-constrained devices, this paper proposes a forest fire detection method, FEDS-YOLOv11n, based on an improved YOLOv11n model. First, the C3k2 module was r...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/10/2989 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849718931354288128 |
|---|---|
| author | Kangqian Zhou Shuihai Jiang |
| author_facet | Kangqian Zhou Shuihai Jiang |
| author_sort | Kangqian Zhou |
| collection | DOAJ |
| description | To address issues in traditional forest fire detection models, such as large parameter sizes, slow detection speed, and unsuitability for resource-constrained devices, this paper proposes a forest fire detection method, FEDS-YOLOv11n, based on an improved YOLOv11n model. First, the C3k2 module was redesigned using the FasterBlock module, replacing C3k2 with C3k2-Faster in both the Backbone network and Neck section to achieve a lightweight model design. Second, an EMA attention mechanism was introduced into the C3k2-Faster module in the Backbone, replacing C3k2-Faster with C3k2-Faster-EMA to compensate for the accuracy loss in small-object detection caused by the lightweight design. Third, the original upsampling module in the Neck was replaced with the lightweight dynamic upsampling operator DySample. Finally, the detection head was improved using the SEAM attention module, replacing the original Detect head with SEAMHead, which enables better handling of occluded objects. The experimental results show that compared to YOLOv11n, the proposed FEDS-YOLOv11n achieves improvements of 0.9% in precision (P), 1.9% in recall (R), 2.1% in mean precision at IoU 0.5 (mAP@0.5), and 2.3% in mean precision at IoU 0.5–0.95 (mAP@0.5–0.95). Additionally, the number of parameters is reduced by 21.32%, GFLOPs are reduced by 26.98%, and FPS increases from 48.2 to 71.8. The FEDS-YOLOv11n model ensures high accuracy while maintaining lower computational complexity and faster inference speed, making it suitable for real-time forest fire detection applications. |
| format | Article |
| id | doaj-art-059e93824e6747409c6f28fcb1983045 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-059e93824e6747409c6f28fcb19830452025-08-20T03:12:15ZengMDPI AGSensors1424-82202025-05-012510298910.3390/s25102989Forest Fire Detection Algorithm Based on Improved YOLOv11nKangqian Zhou0Shuihai Jiang1School of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaSchool of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaTo address issues in traditional forest fire detection models, such as large parameter sizes, slow detection speed, and unsuitability for resource-constrained devices, this paper proposes a forest fire detection method, FEDS-YOLOv11n, based on an improved YOLOv11n model. First, the C3k2 module was redesigned using the FasterBlock module, replacing C3k2 with C3k2-Faster in both the Backbone network and Neck section to achieve a lightweight model design. Second, an EMA attention mechanism was introduced into the C3k2-Faster module in the Backbone, replacing C3k2-Faster with C3k2-Faster-EMA to compensate for the accuracy loss in small-object detection caused by the lightweight design. Third, the original upsampling module in the Neck was replaced with the lightweight dynamic upsampling operator DySample. Finally, the detection head was improved using the SEAM attention module, replacing the original Detect head with SEAMHead, which enables better handling of occluded objects. The experimental results show that compared to YOLOv11n, the proposed FEDS-YOLOv11n achieves improvements of 0.9% in precision (P), 1.9% in recall (R), 2.1% in mean precision at IoU 0.5 (mAP@0.5), and 2.3% in mean precision at IoU 0.5–0.95 (mAP@0.5–0.95). Additionally, the number of parameters is reduced by 21.32%, GFLOPs are reduced by 26.98%, and FPS increases from 48.2 to 71.8. The FEDS-YOLOv11n model ensures high accuracy while maintaining lower computational complexity and faster inference speed, making it suitable for real-time forest fire detection applications.https://www.mdpi.com/1424-8220/25/10/2989YOLOv11nforest firelightweightattention mechanismdynamic upsampling operator |
| spellingShingle | Kangqian Zhou Shuihai Jiang Forest Fire Detection Algorithm Based on Improved YOLOv11n Sensors YOLOv11n forest fire lightweight attention mechanism dynamic upsampling operator |
| title | Forest Fire Detection Algorithm Based on Improved YOLOv11n |
| title_full | Forest Fire Detection Algorithm Based on Improved YOLOv11n |
| title_fullStr | Forest Fire Detection Algorithm Based on Improved YOLOv11n |
| title_full_unstemmed | Forest Fire Detection Algorithm Based on Improved YOLOv11n |
| title_short | Forest Fire Detection Algorithm Based on Improved YOLOv11n |
| title_sort | forest fire detection algorithm based on improved yolov11n |
| topic | YOLOv11n forest fire lightweight attention mechanism dynamic upsampling operator |
| url | https://www.mdpi.com/1424-8220/25/10/2989 |
| work_keys_str_mv | AT kangqianzhou forestfiredetectionalgorithmbasedonimprovedyolov11n AT shuihaijiang forestfiredetectionalgorithmbasedonimprovedyolov11n |