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A forest fire detection method based on improved YOLOv5

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Abstract

With the continuous intensification of global climate change, forest fires have become a significant threat to natural ecosystems and human society. The automatic fire detection system plays a crucial role in the early discovery of forest fires. Current YOLOv5-based fire detection methods encounter several significant challenges: low accuracy and high miss rates in complex backgrounds, inefficiency in real-time applications, and difficulty in detecting small targets, particularly in the early stages of a fire. To address these issues, we propose a forest fire detection method based on improved YOLOv5, aimed at achieving efficient real-time monitoring in resource-constrained environments. First, we add the Convolutional Block Attention Module to improve channel and spatial attention, enhancing the detection of small fire features essential for early detection. Next, we integrate a small target detection layer and the Ghost module into YOLOv5. The small target layer boosts sensitivity to small fire areas, while the Ghost module reduces computational load and parameters, improving feature extraction without sacrificing performance. Finally, we use the SIOU loss function to accelerate model convergence, enhancing overall detection efficiency and precision. Experimental results show that the proposed method achieves an mAP of 88.3% on the Yang et al. dataset, which improves the mAP by 0.9% compared to other YOLOv5-based methods on the same dataset. Model parameter size decreased by 2.8%. On our forest fire detection dataset, the proposed method achieves an mAP of 79.1%. Compared to the YOLOv5s model, this represents a 3.7% improvement in mAP. Model parameter size decreased by 2.3%.

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References

  1. Belavenutti, P., Chung, W., Ager, A.A.: The economic reality of the forest and fuel management deficit on a fire prone western us national forest. J. Environ. Manag. 293, 112825 (2021)

    Article  Google Scholar 

  2. Martínez, J.M., Machuca, M.H., Díaz, R.Z., Silva, F.R., González-Cabán, A.: Economic losses to iberian swine production from forest fires. For. Policy Econom. 13(8), 614–621 (2011)

    Article  Google Scholar 

  3. Alkhatib, Ahmad AA.: A review on forest fire detection techniques. Int. J. Distrib. Sens. Netw. 10(3), 597368 (2014)

    Article  MATH  Google Scholar 

  4. Šerić, L., Stipaničev, D., Štula, M.: Observer network and forest fire detection. Inf. Fus. 12(3), 160–175 (2011)

    Article  MATH  Google Scholar 

  5. Barmpoutis, P., Papaioannou, P., Dimitropoulos, K., Grammalidis, N.: A review on early forest fire detection systems using optical remote sensing. Sensors 20(22), 6442 (2020)

    Article  Google Scholar 

  6. Ha, C., Jeon, G., Jeong, J.: Vision-based smoke detection algorithm for early fire recognition in digital video recording system. In 2011 Seventh International Conference on Signal Image Technology & Internet-Based Systems, p. 209–212. IEEE, (2011)

  7. Voulodimos, A., Doulamis, N., Doulamis, A., Protopapadakis, E.: Deep learning for computer vision: a brief review. Comput. Intell. Neurosci. 2018(1), 7068349 (2018)

    MATH  Google Scholar 

  8. Ultralytics. Ultralytics-yolov5. https://github.com/ultralytics/yolov5, 2024. Accessed: 2024-08-03

  9. Töreyin, B., Dedeoğlu, Y., Güdükbay, U., Cetin, A.E.: Computer vision based method for real-time fire and flame detection. Pattern Recognit. Lett. 27(1), 49–58 (2006)

    Article  MATH  Google Scholar 

  10. Chen, T.H., Wu, P.H., Chiou, Y.C.: An early fire-detection method based on image processing. In 2004 International Conference on Image Processing, 2004. ICIP’04, vol 3, pp. 1707–1710. IEEE, (2004)

  11. Celik, T., Demirel, H., Ozkaramanli, H., Uyguroglu, M.: Fire detection using statistical color model in video sequences. J. Vis. Commun. Image Represent. 18(2), 176–185 (2007)

    Article  MATH  Google Scholar 

  12. Wang, T., Shi, L., Yuan, P., Bu, L., Hou, X.: A new fire detection method based on flame color dispersion and similarity in consecutive frames. In 2017 Chinese Automation Congress (CAC), p. 151–156. IEEE, (2017)

  13. Anshul, G., Singh, A., Kumar, A., Kumar, A., Kapoor, K.: Video flame and smoke based fire detection algorithms: a literature review. Fire Technol. 56(5), 1943–1980 (2020)

    Article  MATH  Google Scholar 

  14. Lou, L., Chen, F., Cheng, P., Huang, Y.: Smoke root detection from video sequences based on multi-feature fusion. J. For. Res. 33(6), 1841–1856 (2022)

    Article  MATH  Google Scholar 

  15. Toreyin, B.U., Cetin, A.E.: Online detection of fire in video. In 2007 IEEE Conference on Computer Vision and Pattern Recognition, p. 1–5. IEEE, (2007)

  16. Cetin, A.E., Merci, B., Günay, O., Töreyin, B.U., Verstockt, S.: Methods and techniques for fire detection: signal, image and video processing perspectives. Academic Press, Cambridge (2016)

    MATH  Google Scholar 

  17. Frizzi, S., Kaabi, R., Bouchouicha, M., Ginoux, J.M., Moreau, E., Fnaiech, F.: Convolutional neural network for video fire and smoke detection. In IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society, p. 877–882. IEEE, (2016)

  18. Maksymiv, O., Rak, T., Peleshko, D.: Real-time fire detection method combining adaboost, lbp and convolutional neural network in video sequence. In 2017 14th international conference the experience of designing and application of CAD Systems in microelectronics (CADSM), pp. 351–353. IEEE, (2017)

  19. Zhong, Z., Wang, M., Shi, Y., Gao, W.: A convolutional neural network-based flame detection method in video sequence. SIViP 12, 1619–1627 (2018)

    Article  MATH  Google Scholar 

  20. Gotthans, J., Gotthans, T., Marsalek, R.: Deep convolutional neural network for fire detection. In 2020 30th international conference radioelektronika (RADIOELEKTRONIKA), pp. 1–6. IEEE, (2020)

  21. Muhammad, K., Ahmad, J., Mehmood, I., Rho, S., Baik, S.W.: Convolutional neural networks based fire detection in surveillance videos. Ieee Access 6, 18174–18183 (2018)

    Article  Google Scholar 

  22. Kim, B., Lee, Joonwhoan: A video-based fire detection using deep learning models. Appl. Sci. 9(14), 2862 (2019)

    Article  MATH  Google Scholar 

  23. Yong, Y., Si, X., Changhua, H., Zhang, Jianxun: A review of recurrent neural networks: Lstm cells and network architectures. Neural Comput. 31(7), 1235–1270 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  24. Aslan, S., Güdükbay, U., Töreyin, B.U., Cetin, A.E.: Early wildfire smoke detection based on motion-based geometric image transformation and deep convolutional generative adversarial networks. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8315–8319. IEEE, (2019)

  25. Yin, DongXu, Cheng, Pengle, Huang, Ying: Yolo-epf: multi-scale smoke detection with enhanced pool former and multiple receptive fields. Digital Signal Process. 149, 104511 (2024)

    Article  Google Scholar 

  26. Chen, X., Zheng, X., Li, Z., Ma, M., Zhang, M.: Self-supervised visual-textual prompt learning for few-shot grading of gastric intestinal metaplasia. Knowl. Based Syst. 301, 112303 (2024)

    Article  Google Scholar 

  27. Zheng, X., Zhang, L., Chunyan, X., Chen, X., Cui, Z.: An attribution graph-based interpretable method for cnns. Neural Netw. 179, 106597 (2024)

    Article  MATH  Google Scholar 

  28. Zhou, X., Wang, K., Li, L.: Review of object detection based on deep learning. Electr. Meas. Technol. 40(11), 89–93 (2017)

    MATH  Google Scholar 

  29. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 580–587, (2014)

  30. Girshick, R.: Fast r-cnn. In Proceedings of the IEEE international conference on computer vision, pp. 1440–1448, (2015)

  31. Leibe, B., Matas, J., Sebe, N., Welling, M.: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV, vol. 9908. Springer, (2016)

  32. Hong, Z., Hamdan, E., Zhao, Y., Ye, T., Pan, H., Cetin, A.E.: Wildfire detection via transfer learning: a survey. Signal Image Video Process. 18(1), 207–214 (2024)

    Article  Google Scholar 

  33. Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: Cbam: convolutional block attention module. In Proceedings of the European conference on computer vision (ECCV), pp. 3–19, (2018)

  34. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7132–7141 (2018)

  35. Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: Eca-net: efficient channel attention for deep convolutional neural networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 11534–11542 (2020)

  36. Lin, S., Liu, M., Tao, Z.: Underwater treasures detection using attention mechanism and improved yolov5. J. Agric. Eng. 37(18), 307–314 (2021)

    MATH  Google Scholar 

  37. Zou, Z.Y., Gai, S.Y., Da, F.P., et al.: Pedestrian occlusion detection algorithm based on attention mechanism. Acta Opt. Sin. 41(15), 157–165 (2021)

    MATH  Google Scholar 

  38. Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., Xu, C.: Ghostnet: more features from cheap operations. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 1580–1589 (2020)

  39. Gevorgyan, Zhora.: Siou loss: more powerful learning for bounding box regression. arXiv preprintarXiv:2205.12740 (2022)

  40. Yang, Jie, Zhu, Wenchao, Sun, Ting, Ren, Xiaojun, Liu, Fang: Lightweight forest smoke and fire detection algorithm based on improved yolov5. PLoS ONE 18(9), e0291359 (2023)

    Article  Google Scholar 

  41. Ren, S.: Faster r-cnn: towards real-time object detection with region proposal networks. arXiv preprintarXiv:1506.01497, (2015)

  42. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778 (2016)

  43. Wang, C.Y., Bochkovskiy, A., Liao, H.Y.M.: Yolov7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 7464–7475 (2023)

  44. Jocher, G. et al.: Yolov8: high-performance object detection. https://github.com/ultralytics/ultralytics, (2023). Accessed: 2024-08-24

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Acknowledgements

This work is supported by the Natural Science Foundation of Shandong Province, China (NO. ZR2022LZH003, ZR2020LZH008, ZR2021MF118), the Key R &D Program of Shandong Province, China (NO. 2021SFGC0104), and the Key R &D Program of Shandong Province, China (Major Scientific and Technological Innovation Project) (NO. 2021CXGC010506).

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  1. School of Information Science and Engineering, Shandong Normal University, Jinan, China

    Zukai Sun, Ruzhi Xu, Xiangwei Zheng, Lifeng Zhang & Yuang Zhang

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  1. Zukai Sun
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Correspondence to Xiangwei Zheng.

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Sun, Z., Xu, R., Zheng, X. et al. A forest fire detection method based on improved YOLOv5. SIViP 19, 136 (2025). https://doi.org/10.1007/s11760-024-03680-6

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