Visible forest fire detection using SBP-YOLOv7

ZHANG Xiaowen, ZHANG Fuquan

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2025, Vol. 49 ›› Issue (3) : 103-109.

PDF(2638 KB)
PDF(2638 KB)
JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2025, Vol. 49 ›› Issue (3) : 103-109. DOI: 10.12302/j.issn.1000-2006.202310031

Visible forest fire detection using SBP-YOLOv7

Author information +
History +

Abstract

【Objective】Forest fires pose a significant threat to the natural environment and human safety, thus timely and accurate detection of fire sources is demanding. However, the complex forest environment characterized by high tree density, ground litter accumulation, and dense canopies creates substantial challenges for effective fire detection. To address these issues, this study proposes a novel forest fire detection method, SBP-YOLOv7.【Method】The proposed method incorporated three key innovations. First, an attention mechanism-enhanced downsampling module (BRA-MP) was introduced to improve feature recognition during downsampling, enhancing the model’s ability to detect small targets by boosting feature representation and semantic relevance. Second, the extended partial convolution efficient layer aggregation module (EP-ELAN) was integrated into the model’s backbone, effectively reducing redundant computations and minimizing model parameters. Finally, a Slim-neck neck module was employed for feature fusion, ensuring high accuracy while lowering computational costs. 【Result】Comparative evaluations on a forest fire dataset demonstrate that the SBP-YOLOv7 model achieves an AP score of 87.0%, representing 2.3% improvement over the original YOLOv7. Additionally, the model reduces parameter count by 22.77% and computational cost by 17.13%.【Conclusion】Compared with the traditional YOLOv7 algorithm, the proposed SBP-YOLOv7 model offers superior accuracy and efficiency, enabling faster and more precise detection of forest fires even in challenging environments.

Key words

deep learning / forest fire detection / forest scenes / YOLOv7 / attention mechanism

Cite this article

Download Citations
ZHANG Xiaowen , ZHANG Fuquan. Visible forest fire detection using SBP-YOLOv7[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY. 2025, 49(3): 103-109 https://doi.org/10.12302/j.issn.1000-2006.202310031

References

[1]
JONES M W, ABATZOGLOU J T, VERAVERBEKE S, et al. Global and regional trends and drivers of fire under climate change[J]. Reviews of Geophysics, 2022, 60(3):e2020RG000726.DOI: 10.1029/2020RG000726.
[2]
SALEH A, ZULKIFLEY M A, HARUN H H, et al. Forest fire surveillance systems:a review of deep learning methods[J]. Heliyon, 2024, 10(1):e23127.DOI: 10.1016/j.heliyon.2023.e23127.
[3]
PAYRA S, SHARMA A, VERMA S. Application of remote sensing to study forest fires[M]. Amsterdam:Elsevier, Atmospheric Remote Sensing. 2023:239-260.DOI: 10.1016/b978-0-323-99262-6.00015-8.
[4]
KONG S Y, DENG J H, YANG L, et al. An attention-based dualen-coding network for fire flame detection using optical remote sensing[J]. Engineering Applications of Artificial Intelligence, 2024,127:107238.DOI: 10.1016/j.engappai.2023.107238.
[5]
岳超, 罗彩访, 舒立福, 等. 全球变化背景下野火研究进展[J]. 生态学报, 2020, 40(2):385-401.
YUE C, LUO C F, SHU L F, et al. A review on wildfire studies in the context of global change[J]. Acta Ecologica Sinica, 2020, 40(2):385-401.DOI: 10.5846/stxb201812202762.
[6]
KASYAP V L, SUMATHI D, ALLURI K, et al. Early detection of forest fire using mixed learning techniques and UAV[J]. Computational Intelligence and Neuroscience, 2022, 2022(1): 3170244.DOI: 10.1155/2022/3170244.
[7]
何乃磊, 张金生, 林文树. 基于深度学习多目标检测技术的林火图像识别研究[J]. 南京林业大学学报(自然科学版), 2024, 48(3):207-218.
HE N L, ZHANG J S, LIN W S. Forest fire image recognition based on deep learning multi-target detection technology[J]. Journal of Nanjing Forestry University (Natural Sciences Edition), 2024, 48(3):207-218.
[8]
ALLISON R S, JOHNSTON J M, CRAIG G, et al. Airborne optical and thermal remote sensing for wildfire detection and monitoring[J]. Sensors, 2016, 16(8):1310.DOI: 10.3390/s16081310.
[9]
KRÜLL W, TOBERA R, WILLMS I, et al. Early forest fire detection and verification using optical smoke,gas and microwave sensors[J]. Procedia Engineering, 2012, 45:584-594.DOI: 10.1016/j.proeng.2012.08.208.
[10]
MOMENI M, SOLEIMANI H, SHAHPARVARI S, et al. Coordinated routing system for fire detection by patrolling trucks with drones[J]. International Journal of Disaster Risk Reduction, 2022,73:102859.DOI: 10.1016/j.ijdrr.2022.102859.
[11]
de la FUENTE R, AGUAYO M M, CONTRERAS-BOLTON C. An optimization-based approach for an integrated forest fire monitoring system with multiple technologies and surveillance drones[J]. European Journal of Operational Research, 2024, 313(2):435-451.DOI: 10.1016/j.ejor.2023.08.008.
[12]
MOHAMMED R K. A real-time forest fire and smoke detection system using deep learning[J]. Int J Nonlinear Anal. 2022(13):2008-6822.
[13]
向俊, 严恩萍, 姜镓伟, 等. 基于全卷积神经网络和低分辨率标签的森林变化检测研究[J]. 南京林业大学学报(自然科学版), 2024, 48(1):187-195.
XIANG J, YAN E P, JIANG J W, et al. Research on forest change detection based on fully convolutional network and low resolution label[J]. Journal of Nanjing Forestry University (Natural Sciences Edition), 2024, 48(1):187-195.DOI: 10.12302/j.issn.1000-2006.202204069.
[14]
KAUR J, SINGH W. Tools,techniques,datasets and application areas for object detection in an image:a review[J]. Multimedia Tools and Applications, 2022, 81(27):38297-38351.DOI: 10.1007/s11042-022-13153-y.
[15]
ZHENG X, CHEN F, LOU L M, et al. Real-time detection of full-scale forest fire smoke based on deep convolution neural network[J]. Remote Sensing, 2022, 14(3):536.DOI: 10.3390/rs14030536.
[16]
WANG C Y, BOCHKOVSKIY A, LIAO H M. YOLOv7:trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]//2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). June 17-24,2023, Vancouver,BC,Canada.IEEE,2023:7464-7475.DOI: 10.1109/CVPR52729.2023.00721.
[17]
ZHU L, WANG X J, KE Z H, et al. BiFormer:vision transformer with bi-level routing attention[C]//2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). June 17-24,2023, Vancouver,BC,Canada.IEEE,2023:10323-10333.DOI: 10.1109/CVPR52729.2023.00995.
[18]
CHEN J R, KAO S H, HE H, et al. Run,don’t walk:chasing higher FLOPS for faster neural networks[C]//2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). June 17-24,2023, Vancouver,BC,Canada.IEEE,2023:12021-12031.DOI: 10.1109/CVPR52729.2023.01157.
[19]
HAN K, WANG Y H, TIAN Q, et al. GhostNet:more features from cheap operations[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). June 13-19,2020, Seattle,WA,USA.IEEE,2020:1577-1586.DOI: 10.1109/CVPR42600.2020.00165.
[20]
LI H L, LI J, WEI H B, et al. Slim-neck by GSConv:a better design paradigm of detector architectures for autonomous vehicles[J]. arXiv preprint arXiv.2022(2206):02424.
[21]
SELVARAJU R R, COGSWELL M, DAS A, et al. Grad-CAM:visual explanations from deep networks via gradient-based localization[J]. International Journal of Computer Vision, 2020, 128(2):336-359.DOI: 10.1007/s11263-019-01228-7.
PDF(2638 KB)

Accesses

Citation

Detail

Sections
Recommended
The full text is translated into English by AI, aiming to facilitate reading and comprehension. The core content is subject to the explanation in Chinese.

/