Multiscale wildfire and smoke detection in complex drone forest environments based on YOLOv8
Abstract Global climate change has triggered frequent extreme weather events, leading to a significant increase in the frequency and intensity of forest fires. Traditional fire monitoring methods such as manual inspections, sensor technologies, and remote sensing satellites have limitations. With th...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-86239-w |
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| author | Wenyu Zhu Shanwei Niu Jixiang Yue Yangli Zhou |
| author_facet | Wenyu Zhu Shanwei Niu Jixiang Yue Yangli Zhou |
| author_sort | Wenyu Zhu |
| collection | DOAJ |
| description | Abstract Global climate change has triggered frequent extreme weather events, leading to a significant increase in the frequency and intensity of forest fires. Traditional fire monitoring methods such as manual inspections, sensor technologies, and remote sensing satellites have limitations. With the advancement of drone technology and deep learning, using drones combined with artificial intelligence for fire monitoring has become mainstream. This paper proposes an improved YOLOv8-based model that incorporates local convolution instead of full convolution in the C2F module and integrates the EMA module to enhance the feature channel interaction modeling capability and contextual information utilization, thereby reducing model complexity and increasing efficiency. Additionally, in order to address the risk of false positives and missed detections caused by vegetation, terrain, and lighting changes in forests, we have introduced the AgentAttention module in the Backbone. This module combines Softmax and linear attention to optimize feature extraction, improving the model’s accuracy and robustness. Furthermore, in order to tackle the challenges of detecting flames and smoke at different scales and angles, we have designed the BiFormer module, which adaptively fuses global and local features, significantly enhancing the model’s multi-scale and multi-angle detection capability. Experimental results show that the improved model achieves Precision and Recall of 93.57% and 88.51%, respectively, representing improvements of 5.05% and 2.72% over the original model. It also optimizes FPS, GFLOPs, and Params by 14.3%, 25%, and 19.7%, respectively. This research has significant application prospects in forest fire early warning, emergency response, and loss reduction, while also providing strong technical support for forest resource protection and public safety. |
| format | Article |
| id | doaj-art-27b3c4d7986142249ad8fbcd8a5107fb |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-27b3c4d7986142249ad8fbcd8a5107fb2025-01-19T12:22:30ZengNature PortfolioScientific Reports2045-23222025-01-0115112010.1038/s41598-025-86239-wMultiscale wildfire and smoke detection in complex drone forest environments based on YOLOv8Wenyu Zhu0Shanwei Niu1Jixiang Yue2Yangli Zhou3School of Mechanical and Electrical Engineering, China University of Petroleum HuadongCollege of Information Science and Technology, Gansu Agricultural UniversityShandong Provincial Engineering Research Center for Green Manufacturing and Intelligent Control, Shandong Institute of Petroleum and Chemical TechnologySchool of Mechanical and Electrical Engineering, China University of Petroleum HuadongAbstract Global climate change has triggered frequent extreme weather events, leading to a significant increase in the frequency and intensity of forest fires. Traditional fire monitoring methods such as manual inspections, sensor technologies, and remote sensing satellites have limitations. With the advancement of drone technology and deep learning, using drones combined with artificial intelligence for fire monitoring has become mainstream. This paper proposes an improved YOLOv8-based model that incorporates local convolution instead of full convolution in the C2F module and integrates the EMA module to enhance the feature channel interaction modeling capability and contextual information utilization, thereby reducing model complexity and increasing efficiency. Additionally, in order to address the risk of false positives and missed detections caused by vegetation, terrain, and lighting changes in forests, we have introduced the AgentAttention module in the Backbone. This module combines Softmax and linear attention to optimize feature extraction, improving the model’s accuracy and robustness. Furthermore, in order to tackle the challenges of detecting flames and smoke at different scales and angles, we have designed the BiFormer module, which adaptively fuses global and local features, significantly enhancing the model’s multi-scale and multi-angle detection capability. Experimental results show that the improved model achieves Precision and Recall of 93.57% and 88.51%, respectively, representing improvements of 5.05% and 2.72% over the original model. It also optimizes FPS, GFLOPs, and Params by 14.3%, 25%, and 19.7%, respectively. This research has significant application prospects in forest fire early warning, emergency response, and loss reduction, while also providing strong technical support for forest resource protection and public safety.https://doi.org/10.1038/s41598-025-86239-wForest fire monitoringSmoke detectionYOLOv8UAVDeep learningC2F module |
| spellingShingle | Wenyu Zhu Shanwei Niu Jixiang Yue Yangli Zhou Multiscale wildfire and smoke detection in complex drone forest environments based on YOLOv8 Scientific Reports Forest fire monitoring Smoke detection YOLOv8 UAV Deep learning C2F module |
| title | Multiscale wildfire and smoke detection in complex drone forest environments based on YOLOv8 |
| title_full | Multiscale wildfire and smoke detection in complex drone forest environments based on YOLOv8 |
| title_fullStr | Multiscale wildfire and smoke detection in complex drone forest environments based on YOLOv8 |
| title_full_unstemmed | Multiscale wildfire and smoke detection in complex drone forest environments based on YOLOv8 |
| title_short | Multiscale wildfire and smoke detection in complex drone forest environments based on YOLOv8 |
| title_sort | multiscale wildfire and smoke detection in complex drone forest environments based on yolov8 |
| topic | Forest fire monitoring Smoke detection YOLOv8 UAV Deep learning C2F module |
| url | https://doi.org/10.1038/s41598-025-86239-w |
| work_keys_str_mv | AT wenyuzhu multiscalewildfireandsmokedetectionincomplexdroneforestenvironmentsbasedonyolov8 AT shanweiniu multiscalewildfireandsmokedetectionincomplexdroneforestenvironmentsbasedonyolov8 AT jixiangyue multiscalewildfireandsmokedetectionincomplexdroneforestenvironmentsbasedonyolov8 AT yanglizhou multiscalewildfireandsmokedetectionincomplexdroneforestenvironmentsbasedonyolov8 |