REAL-TIME UAV EARLY SMOKE DETECTION WITH A LIGHTWEIGHT, FEATURE-REFINED DETECTOR

Norkobil Saydirasulov; Dusmurod Kilichev

Authors

Saydirasulov Norkobil Gachon University, Sujeong-Gu, Seongnam-Si 461-701, Gyeonggi-Do, Republic of Korea
Kilichev Dusmurod Gachon University, Sujeong-Gu, Seongnam-Si 461-701, Gyeonggi-Do, Republic of Korea

Keywords:

UAV smoke detection, lightweight YOLOv8, BiFormer attention, Ghost Shuffle Convolution, WIoU v3, real-time wildfire monitoring

Abstract

Wildfires are highly destructive, making early smoke detection crucial for rapid suppression. Existing UAV-based methods face challenges such as slow inference, limited accuracy, and weak sensitivity to small or diffuse smoke. This study introduces a lightweight YOLOv8-based detector enhanced with (1) Wise-IoU v3 for robust localization, (2) Ghost Shuffle Convolution for reduced computation and real-time efficiency, and (3) BiFormer attention to emphasize smoke cues while suppressing clutter. On a UAV smoke dataset, the model achieves 79.4% AP (+3.3 over baseline), with notable performance on small (71.3%) and large (92.6%) targets. The results highlight improved accuracy and efficiency, supporting practical real-time UAV-based wildfire monitoring.

References

K. Hoover and L. A. Hanson, Wildfire Statistics; Congressional Research Service (CRS) in Focus. Washington, DC, USA: Congressional Research Service (CRS), 2023.

X. Xu, F. Li, Z. Lin, and X. Song, “Holocene fire history in China: Responses to climate change and human activities,” Sci. Total Environ., vol. 753, p. 142019, 2020, doi: 10.1016/j.scitotenv.2020.142019.

A. B. Abdusalomov, B. M. S. Islam, R. Nasimov, M. Mukhiddinov, and T. K. Whangbo, “An improved forest fire detection method based on the detectron2 model and a deep learning approach,” Sensors, vol. 23, p. 1512, 2023, doi: 10.3390/s23031512.

Y. Hu et al., “Fast forest fire smoke detection using MVMNet,” Knowl.-Based Syst., vol. 241, p. 108219, 2022, doi: 10.1016/j.knosys.2021.108219.

M. Mukhiddinov, A. B. Abdusalomov, and J. Cho, “Automatic fire detection and notification system based on improved YOLOv4 for the blind and visually impaired,” Sensors, vol. 22, p. 3307, 2022, doi: 10.3390/s22093307.

K. Avazov, M. Mukhiddinov, F. Makhmudov, and Y. I. Cho, “Fire detection method in smart city environments using a deep-learning-based approach,” Electronics, vol. 11, p. 73, 2021, doi: 10.3390/electronics11010073.

F. Zhang et al., “Integrating multiple factors to optimize watchtower deployment for wildfire detection,” Sci. Total Environ., vol. 737, p. 139561, 2020, doi: 10.1016/j.scitotenv.2020.139561.

J. Yao et al., “Predicting the minimum height of forest fire smoke within the atmosphere using machine learning and data from the CALIPSO satellite,” Remote Sens. Environ., vol. 206, pp. 98–106, 2018, doi: 10.1016/j.rse.2017.12.012.

M. Mukhiddinov, A. B. Abdusalomov, and J. Cho, “A wildfire smoke detection system using unmanned aerial vehicle images based on the optimized YOLOv5,” Sensors, vol. 22, p. 9384, 2022, doi: 10.3390/s22239384.

Y. Xie et al., “Efficient video fire detection exploiting motion-flicker-based dynamic features and deep static features,” IEEE Access, vol. 8, pp. 81904–81917, 2020, doi: 10.1109/ACCESS.2020.2991327.

A. Abdusalomov, N. Baratov, A. Kutlimuratov, and T. K. Whangbo, “An improvement of the fire detection and classification method using YOLOv3 for surveillance systems,” Sensors, vol. 21, p. 6519, 2021, doi: 10.3390/s21196519.

S. Khan and A. Khan, “Ffirenet: Deep learning based forest fire classification and detection in smart cities,” Symmetry, vol. 14, p. 2155, 2022, doi: 10.3390/sym14102155.

M. Jeon, H. S. Choi, J. Lee, and M. Kang, “Multi-scale prediction for fire detection using convolutional neural network,” Fire Technol., vol. 57, pp. 2533–2551, 2021, doi: 10.1007/s10694-021-01122-2.

N. S. Saydirasulovich et al., “A YOLOv6-based improved fire detection approach for smart city environments,” Sensors, vol. 23, p. 3161, 2023, doi: 10.3390/s23063161.

A. Khan, S. Khan, B. Hassan, and Z. Zheng, “CNN-based smoker classification and detection in smart city application,” Sensors, vol. 22, p. 892, 2022, doi: 10.3390/s22030892.

F. M. Talaat and H. ZainEldin, “An improved fire detection approach based on YOLO-v8 for smart cities,” Neural Comput. Appl., vol. 5, pp. 20939–20954, 2023, doi: 10.1007/s00521-023-08023-7.

G. Liu, H. Yuan, and L. Huang, “A fire alarm judgment method using multiple smoke alarms based on Bayesian estimation,” Fire Saf. J., vol. 136, p. 103733, 2023, doi: 10.1016/j.firesaf.2023.103733.

J. Redmon and A. Farhadi, “YOLOv3: An incremental improvement,” arXiv preprint, arXiv:1804.02767, 2018.

K. He, X. Zhang, S. Ren, and J. Sun, “Spatial pyramid pooling in deep convolutional networks for visual recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, no. 9, pp. 1904–1916, 2015, doi: 10.1109/TPAMI.2015.2389824.

S. Liu, L. Qi, H. Qin, J. Shi, and J. Jia, “Path aggregation network for instance segmentation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Salt Lake City, UT, USA, Jun. 18–23, 2018, pp. 8759–8768.

Z. Zheng et al., “Distance-IoU loss: Faster and better learning for bounding box regression,” in Proc. AAAI Conf. Artif. Intell., New York, NY, USA, Feb. 7–12, 2020, pp. 12993–13000.

X. Li et al., “Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection,” arXiv preprint, arXiv:2006.04388, 2020.

C. Feng, Y. Zhong, Y. Gao, M. R. Scott, and W. Huang, “TOOD: Task-aligned one-stage object detection,” in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), Montreal, QC, Canada, Oct. 10–17, 2021, pp. 3490–3499.

J. Pan, X. Ou, and L. Xu, “A collaborative region detection and grading framework for forest fire smoke using weakly supervised fine segmentation and lightweight Faster-RCNN,” Forests, vol. 12, p. 768, 2021, doi: 10.3390/f12060768.

T. Li, E. Zhao, J. Zhang, and C. Hu, “Detection of wildfire smoke images based on a densely dilated convolutional network,” Electronics, vol. 8, p. 1131, 2019, doi: 10.3390/electronics8101131.

T. Kanand, G. Kemper, R. König, and H. Kemper, “Wildfire detection and disaster monitoring system using UAS and sensor fusion technologies,” Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., vol. 43, pp. 1671–1675, 2020, doi: 10.5194/isprs-archives-XLIII-B3-2020-1671-2020.

E. U. Rahman et al., “Computer vision-based wildfire smoke detection using UAVs,” Math. Probl. Eng., vol. 2021, p. 9977939, 2021, doi: 10.1155/2021/9977939.

G. Jocher, “YOLOv8,” GitHub repository, Ultralytics, 2023. [Online]. Available: https://github.com/ultralytics/ultralytics

Z. Tong, Y. Chen, Z. Xu, and R. Yu, “Wise-IoU: Bounding box regression loss with dynamic focusing mechanism,” arXiv preprint, arXiv:2301.10051, 2023.

L. Zhu, X. Wang, Z. Ke, W. Zhang, and R. W. Lau, “BiFormer: Vision transformer with bi-level routing attention,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Vancouver, BC, Canada, Jun. 17–24, 2023, pp. 10323–10333.

H. Li, J. Li, H. Wei, Z. Liu, Z. Zhan, and Q. Ren, “Slim-neck by GSConv: A better design paradigm of detector architectures for autonomous vehicles,” arXiv preprint, arXiv:2206.02424, 2022.

Töreyin, B.U.; Dedeoğlu, Y.; Güdükbay, U.; Cetin, A.E. Computer vision based method for real-time fire and flame detection. Pattern Recognit. Lett. 2006, 27, 49–58. [Google Scholar] [CrossRef]