Precision-Boosted Forest Fire Target Detection via Enhanced YOLOv8 Model
Source abstract
Abstract
Forest fires present a significant challenge to ecosystems, particularly due to factors like tree cover that complicate fire detection tasks. While fire detection technologies, like YOLO, are widely used in forest protection, capturing diverse and complex flame features remains challenging. Therefore, we propose an enhanced YOLOv8 multiscale forest fire detection method. This involves adjusting the network structure and integrating Deformable Convolution and SCConv modules to better adapt to forest fire complexities. Additionally, we introduce the Coordinate Attention mechanism in the Detection module to more effectively capture feature information and enhance model accuracy. We adopt the WIoU v3 loss function and implement a dynamically non-monotonic mechanism to optimize gradient allocation strategies. Our experimental results demonstrate that our model achieves a mAP of 90.02%, approximately 5.9% higher than the baseline YOLOv8 network. This method significantly improves forest fire detection accuracy, reduces False Positive rates, and demonstrates excellent applicability in real forest fire scenarios.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Fire Detection and Safety Systems · Fire effects on ecosystems · Video Surveillance and Tracking Methods
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer and Communication Technology, Lanzhou University of Technology, Lanzhou 730050, China
Department of Applied Mathematics, Lanzhou University of Technology, Lanzhou 730050, China
Funding
National Natural Science Foundation of China · Chinese National College Students Innovation and Entrepreneurship Training Program
References (37)
Show source-provided references
1. Forest functions, ecosystem stability and management10.1016/S0378-1127(00)00377-7 ↗
2. Effect of forest bathing trips on human immune function10.1007/s12199-008-0068-3 ↗
3. The role of fire disturbance for global vegetation dynamics: Coupling fire into a Dynamic Global Vegetation Model10.1046/j.1466-822X.2001.00175.x ↗
4. Carbon sequestration potential and CO2 fluxes in a tropical forest ecosystem10.1016/j.ecoleng.2022.106541 ↗
5. Watson, A.J., and Lovelock, J.E. (2013). The dependence of flame spread and probability of ignition on atmospheric oxygen: An experimental investigation. Fire Phenom. Earth Syst. Interdiscip. Guide Fire Sci., 273–287.10.1002/9781118529539.ch14 ↗
6. Using wireless sensor networks for reliable forest fires detection10.1016/j.procs.2013.06.104 ↗
7. A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques10.1139/cjfr-2014-0347 ↗
8. Satellite remote sensing contributions to wildland fire science and management10.1007/s40725-020-00116-5 ↗
9. Chen, Y., Zhang, Y., Xin, J., Wang, G., Mu, L., Yi, Y., Liu, H., and Liu, D. (2019, January 19–21). UAV image-based forest fire detection approach using convolutional neural network. Proceedings of the 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA), Xi’an, China.10.1109/ICIEA.2019.8833958 ↗
10. Forest fire detection and identification using image processing and SVM
11. Efficient flame detection based on static and dynamic texture analysis in forest fire detection10.1007/s10694-017-0683-x ↗
12. Video fire detection based on Gaussian Mixture Model and multi-color features10.1007/s11760-017-1102-y ↗
13. Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area10.1016/j.ecolind.2021.107869 ↗
14. An IoT based forest fire detection system using integration of cat swarm with LSTM model10.1016/j.comcom.2023.08.020 ↗
15. Barmpoutis, P., Dimitropoulos, K., Kaza, K., and Grammalidis, N. (2019, January 12–17). Fire detection from images using faster R-CNN and multidimensional texture analysis. Proceedings of the ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.10.1109/ICASSP.2019.8682647 ↗
16. Alice, K., Thillaivanan, A., Rao, G.R.K., Rajalakshmi, S., Singh, K., and Rastogi, R. (2023, January 4–6). Automated Forest Fire Detection using Atom Search Optimizer with Deep Transfer Learning Model. Proceedings of the 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), Salem, India.10.1109/ICAAIC56838.2023.10141524 ↗
17. Lin, J., Lin, H., and Wang, F. (2023). A semi-supervised method for real-time forest fire detection algorithm based on adaptively spatial feature fusion. Forests, 14.10.3390/f14020361 ↗
18. Ghali, R., Akhloufi, M.A., and Mseddi, W.S. (2022). Deep learning and transformer approaches for UAV-based wildfire detection and segmentation. Sensors, 22.10.3390/s22051977 ↗
19. Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y. (2017, January 22–29). Deformable convolutional networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.10.1109/ICCV.2017.89 ↗
20. Li, J., Wen, Y., and He, L. (2023, January 17–24). SCConv: Spatial and Channel Reconstruction Convolution for Feature Redundancy. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.10.1109/CVPR52729.2023.00596 ↗
21. Hou, Q., Zhou, D., and Feng, J. (2021, January 19–25). Coordinate attention for efficient mobile network design. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Virtual.10.1109/CVPR46437.2021.01350 ↗
22. Tong, Z., Chen, Y., Xu, Z., and Yu, R. (2023). Wise-IoU: Bounding Box Regression Loss with Dynamic Focusing Mechanism. arXiv.
23. Chino, D.Y., Avalhais, L.P., Rodrigues, J.F., and Traina, A.J. (2015, January 26–29). Bowfire: Detection of fire in still images by integrating pixel color and texture analysis. Proceedings of the 2015 28th SIBGRAPI Conference on Graphics, Patterns and Images, Salvador, Brazil.10.1109/SIBGRAPI.2015.19 ↗
24. Ultralytics (2023, June 21). YOLOv8. Available online: https://docs.ultralytics.com/.
25. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27–30). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.91 ↗
26. JOCHER (2022, December 24). Network Data. Available online: https://github.com/ultralytics/yolov5.
27. Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2023, January 17–24). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.10.1109/CVPR52729.2023.00721 ↗
28. Ge, Z., Liu, S., Wang, F., Li, Z., and Sun, J. (2021). Yolox: Exceeding yolo series in 2021. arXiv.
29. Zhang, H., Cisse, M., Dauphin, Y.N., and Lopez-Paz, D. (2017). mixup: Beyond empirical risk minimization. arXiv.10.1007/978-1-4899-7687-1_79 ↗
30. DS-YOLOv8-Based Object Detection Method for Remote Sensing Images10.1109/ACCESS.2023.3330844 ↗
31. Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., and Ren, D. (2020, January 7–12). Distance-IoU loss: Faster and better learning for bounding box regression. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.10.1609/aaai.v34i07.6999 ↗
32. Focal and efficient IOU loss for accurate bounding box regression10.1016/j.neucom.2022.07.042 ↗
33. Gevorgyan, Z. (2022). SIoU Loss: More Powerful Learning for Bounding Box Regression. arXiv.
34. Girshick, R. (2015, January 7–13). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.10.1109/ICCV.2015.169 ↗
35. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11–14). Ssd: Single shot multibox detector. Proceedings of the Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands. Proceedings, Part I 14.10.1007/978-3-319-46448-0_2 ↗
36. Wildland Fire Detection and Monitoring Using a Drone-Collected RGB/IR Image Dataset10.1109/ACCESS.2022.3222805 ↗
37. Drone imagery forest fire detection and classification using modified deep learning model10.2298/TSCI22S1411M ↗