Detection Model for Cotton Picker Fire Recognition Based on Lightweight Improved YOLOv11
In response to the limited research on fire detection in cotton pickers and the issue of low detection accuracy in visual inspection, this paper proposes a computer vision-based detection method. The method is optimized according to the structural characteristics of cotton pickers, and a lightweight...
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| Format: | Article |
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MDPI AG
2025-07-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/15/15/1608 |
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| author | Zhai Shi Fangwei Wu Changjie Han Dongdong Song Yi Wu |
| author_facet | Zhai Shi Fangwei Wu Changjie Han Dongdong Song Yi Wu |
| author_sort | Zhai Shi |
| collection | DOAJ |
| description | In response to the limited research on fire detection in cotton pickers and the issue of low detection accuracy in visual inspection, this paper proposes a computer vision-based detection method. The method is optimized according to the structural characteristics of cotton pickers, and a lightweight improved YOLOv11 algorithm is designed for cotton fire detection in cotton pickers. The backbone of the model is replaced with the MobileNetV2 network to achieve effective model lightweighting. In addition, the convolutional layers in the original C3k2 block are optimized using partial convolutions to reduce computational redundancy and improve inference efficiency. Furthermore, a visual attention mechanism named CBAM-ECA (Convolutional Block Attention Module-Efficient Channel Attention) is designed to suit the complex working conditions of cotton pickers. This mechanism aims to enhance the model’s feature extraction capability under challenging environmental conditions, thereby improving overall detection accuracy. To further improve localization performance and accelerate convergence, the loss function is also modified. These improvements enable the model to achieve higher precision in fire detection while ensuring fast and accurate localization. Experimental results demonstrate that the improved model reduces the number of parameters by 38%, increases the frame processing speed (FPS) by 13.2%, and decreases the computational complexity (GFLOPs) by 42.8%, compared to the original model. The detection accuracy for flaming combustion, smoldering combustion, and overall detection is improved by 1.4%, 3%, and 1.9%, respectively, with an increase of 2.4% in mAP (mean average precision). Compared to other models—YOLOv3-tiny, YOLOv5, YOLOv8, and YOLOv10—the proposed method achieves higher detection accuracy by 5.9%, 7%, 5.9%, and 5.3%, respectively, and shows improvements in mAP by 5.4%, 5%, 4.8%, and 6.3%. The improved detection algorithm maintains high accuracy while achieving faster inference speed and fewer model parameters. These improvements lay a solid foundation for fire prevention and suppression in cotton collection boxes on cotton pickers. |
| format | Article |
| id | doaj-art-1ecee3c9a4ef4d5d985497352b610a85 |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-1ecee3c9a4ef4d5d985497352b610a852025-08-20T03:36:01ZengMDPI AGAgriculture2077-04722025-07-011515160810.3390/agriculture15151608Detection Model for Cotton Picker Fire Recognition Based on Lightweight Improved YOLOv11Zhai Shi0Fangwei Wu1Changjie Han2Dongdong Song3Yi Wu4College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, ChinaCollege of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, ChinaCollege of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, ChinaCollege of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, ChinaCollege of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, ChinaIn response to the limited research on fire detection in cotton pickers and the issue of low detection accuracy in visual inspection, this paper proposes a computer vision-based detection method. The method is optimized according to the structural characteristics of cotton pickers, and a lightweight improved YOLOv11 algorithm is designed for cotton fire detection in cotton pickers. The backbone of the model is replaced with the MobileNetV2 network to achieve effective model lightweighting. In addition, the convolutional layers in the original C3k2 block are optimized using partial convolutions to reduce computational redundancy and improve inference efficiency. Furthermore, a visual attention mechanism named CBAM-ECA (Convolutional Block Attention Module-Efficient Channel Attention) is designed to suit the complex working conditions of cotton pickers. This mechanism aims to enhance the model’s feature extraction capability under challenging environmental conditions, thereby improving overall detection accuracy. To further improve localization performance and accelerate convergence, the loss function is also modified. These improvements enable the model to achieve higher precision in fire detection while ensuring fast and accurate localization. Experimental results demonstrate that the improved model reduces the number of parameters by 38%, increases the frame processing speed (FPS) by 13.2%, and decreases the computational complexity (GFLOPs) by 42.8%, compared to the original model. The detection accuracy for flaming combustion, smoldering combustion, and overall detection is improved by 1.4%, 3%, and 1.9%, respectively, with an increase of 2.4% in mAP (mean average precision). Compared to other models—YOLOv3-tiny, YOLOv5, YOLOv8, and YOLOv10—the proposed method achieves higher detection accuracy by 5.9%, 7%, 5.9%, and 5.3%, respectively, and shows improvements in mAP by 5.4%, 5%, 4.8%, and 6.3%. The improved detection algorithm maintains high accuracy while achieving faster inference speed and fewer model parameters. These improvements lay a solid foundation for fire prevention and suppression in cotton collection boxes on cotton pickers.https://www.mdpi.com/2077-0472/15/15/1608cotton pickerfirevisual detectionYOLOv11 |
| spellingShingle | Zhai Shi Fangwei Wu Changjie Han Dongdong Song Yi Wu Detection Model for Cotton Picker Fire Recognition Based on Lightweight Improved YOLOv11 Agriculture cotton picker fire visual detection YOLOv11 |
| title | Detection Model for Cotton Picker Fire Recognition Based on Lightweight Improved YOLOv11 |
| title_full | Detection Model for Cotton Picker Fire Recognition Based on Lightweight Improved YOLOv11 |
| title_fullStr | Detection Model for Cotton Picker Fire Recognition Based on Lightweight Improved YOLOv11 |
| title_full_unstemmed | Detection Model for Cotton Picker Fire Recognition Based on Lightweight Improved YOLOv11 |
| title_short | Detection Model for Cotton Picker Fire Recognition Based on Lightweight Improved YOLOv11 |
| title_sort | detection model for cotton picker fire recognition based on lightweight improved yolov11 |
| topic | cotton picker fire visual detection YOLOv11 |
| url | https://www.mdpi.com/2077-0472/15/15/1608 |
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