Research Article
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An AI-based Image Recognition System for Early Detection of Forest and Field Fires

Year 2023, , 48 - 56, 26.12.2023
https://doi.org/10.33904/ejfe.1322396

Abstract

Forest fires and field fires (agricultural areas, grasslands, etc.) have severe global implications, causing significant environmental and economic harm. Traditional fire detection methods often rely on human personnel, which can pose safety risks and reduce their efficiency in large-scale monitoring. There is an urgent need for real-time fire detection technology to address these challenges and minimize losses. In this research, we propose the utilization of artificial intelligence techniques, specifically Deep Learning with Convolutional Neural Networks (CNN), to tackle this issue. Our proposed system analyzes real-time images captured by IP cameras and stored on a cloud server. Its primary objective is to detect signs of fires and promptly notify users through a mobile application, ensuring timely awareness. We meticulously assembled a dataset to train our model by merging three existing datasets comprising both fire and non-fire images. Also, we incorporated images that could potentially be misinterpreted as fire, such as red trees, individuals wearing red clothing, and red flags. Furthermore, we supplemented the dataset with images of unaffected areas obtained from online sources. The final dataset consisted of 1,588 fire images and 909 non-fire images. During evaluations, our model achieved an accuracy of 93.07%. This enables effective detection, thus rapid intervention and damage reduction. It is a proactive and preventive solution to combat these devastating fires.

References

  • Abdusalomov, A.B., Islam, B.M.S., Nasimov, R., Mukhiddinov, M., Whangbo, T.K. 2023. An improved forest fire detection method based on the detectron2 model and a deep learning approach. Sensors, 23(3):1512. 10.3390/s23031512.
  • Andersen, A.N. 2021. Faunal responses to fire in Australian tropical savannas: Insights from field experiments and their lessons for conservation management. Diversity and Distributions, 27(5):828-843. 10.1111/ddi.13198.
  • Aslan, S., Güdükbay, U., Töreyin, B.U., Cetin, E. 2019. Deep convolu- tional generative adversarial networks based flame detection in video. arXiv preprint arXiv:1902.01824.
  • Barmpoutis, P., Periklis Papaioannou, P., Dimitropoulos, K., Gram-malidis, N. 2020. A review on early forest fire detection systems using optical remote sensing. Sensors, 20(22):6442. 10.3390/s20226442.
  • Bouguettaya, A., Zarzour, H., Taberkit, A.M., Kechida, A. 2022. A review on early wildfire detection from unmanned aerial vehicles using deep learning-based computer vision algorithms. Signal Processing, 190:108309. 10.1016/j.sigpro.2021.108309.
  • Burke, M., Driscoll, A., Heft-Neal, S., Xue, J., Burney, J., Wara, M. 2021. The changing risk and burden of wildfire in the United States. Proceedings of the National Academy of Sciences, 118(2):e2011048118. 10.1073/pnas.2011048118.
  • Geetha, S., Abhishek, C.S., Akshayanat, C.S. 2021. Machine vision-based fire detection techniques: A survey. Fire technology, 57:591–623. 10.1007/ s10694-020-01064-z.
  • Guede-Fernández, F., Martins, L., de Almeida, R.V., Gamboa, H., Vieira, P. 2021. A deep learning based object identification system for forest fire detection. Fire, 4(4):75. 10.3390/fire4040075.
  • Kim, B., Lee. J. 2019. A video-based fire detection using deep learning models. Applied Sciences, 9(14):2862. 10.3390/app9142862.
  • Kukuk, S.B., Kilimci, Z.H. 2021. Comprehensive analysis of forest fire detection using deep learning models and conventional machine learning algorithms. International Journal of Computational and Experimental Science and Engineering, 7(2):84-4. 10.22399/ijcesen.950045.
  • Labed, S., Touati, H., Dif., R. 2022. Plant recognition using data augmentation and convolutional neural network. In International Symposium on Modelling and Implementation of Complex Systems, 192–204. Springer. 10.1007/978-3-031-18516-8_14.
  • Li, Z., Jiang, H., Mei, Q., Li, Z. 2022. Forest fire recognition based on lightweight convolutional neural network. Journal of Internet Technology, 23(5):1147–1154.
  • Madhwaraj, K.G., Asha, V., Vignesh, A., Akshay, S. 2023. Forest fire detection using machine learning. In 2023 IEEE 12th International Conference on Communication Systems and Network Technologies (CSNT), 191–196. IEEE. 10.1109/CSNT57126. 2023.10134684.
  • Mohammed, R.K. 2022. A real-time forest fire and smoke detection system using deep learning. International Journal of Nonlinear Analysis and Applications, 13(1):2053–2063.
  • Pan, H., Badawi, D., Zhang, X., Cetin, A.E. 2020. Additive neural network for forest fire detection. Signal, Image and Video Processing, 14:675–682. 10.1007/s11760-019-01600-7.
  • Pranamurti, H., Murti, A., Setianingsih, C. 2019. Fire detection use CCTV with image processing based raspberry pi. In Journal of Physics: Conference Series, volume 1201, page 012015. IOP Publishing.
  • Regi, M., Varghese, R.G., Sidharth, V. 2018. Deep learning based fire detection system. International Journal of Knowledge Based Computer Systems, 76(1):18.
  • Saeed, F., Paul, A., Karthigaikumar, P., Nayyar, A. 2020. Convolutional neural network based early fire detection. Multimedia Tools and Applications, 79:9083–9099. 10.1007/s11042-019-07785-w.
  • Sairi, A., Labed, S., Miles, B., Kout, A. 2023. A review on early forest fire detection using IoT-enabled WSN. In 2023 International Conference on Advances in Electronics, Control and Communication Systems, pp 1–6. IEEE.
  • Shambhu, S., Koundal, D., Das, P., Sharma, C. 2021. Binary classification of covid-19 ct images using cnn: Covid diagnosis using ct. International Journal of E-Health and Medical Communications (IJEHMC), 13(2):1–13.
Year 2023, , 48 - 56, 26.12.2023
https://doi.org/10.33904/ejfe.1322396

Abstract

References

  • Abdusalomov, A.B., Islam, B.M.S., Nasimov, R., Mukhiddinov, M., Whangbo, T.K. 2023. An improved forest fire detection method based on the detectron2 model and a deep learning approach. Sensors, 23(3):1512. 10.3390/s23031512.
  • Andersen, A.N. 2021. Faunal responses to fire in Australian tropical savannas: Insights from field experiments and their lessons for conservation management. Diversity and Distributions, 27(5):828-843. 10.1111/ddi.13198.
  • Aslan, S., Güdükbay, U., Töreyin, B.U., Cetin, E. 2019. Deep convolu- tional generative adversarial networks based flame detection in video. arXiv preprint arXiv:1902.01824.
  • Barmpoutis, P., Periklis Papaioannou, P., Dimitropoulos, K., Gram-malidis, N. 2020. A review on early forest fire detection systems using optical remote sensing. Sensors, 20(22):6442. 10.3390/s20226442.
  • Bouguettaya, A., Zarzour, H., Taberkit, A.M., Kechida, A. 2022. A review on early wildfire detection from unmanned aerial vehicles using deep learning-based computer vision algorithms. Signal Processing, 190:108309. 10.1016/j.sigpro.2021.108309.
  • Burke, M., Driscoll, A., Heft-Neal, S., Xue, J., Burney, J., Wara, M. 2021. The changing risk and burden of wildfire in the United States. Proceedings of the National Academy of Sciences, 118(2):e2011048118. 10.1073/pnas.2011048118.
  • Geetha, S., Abhishek, C.S., Akshayanat, C.S. 2021. Machine vision-based fire detection techniques: A survey. Fire technology, 57:591–623. 10.1007/ s10694-020-01064-z.
  • Guede-Fernández, F., Martins, L., de Almeida, R.V., Gamboa, H., Vieira, P. 2021. A deep learning based object identification system for forest fire detection. Fire, 4(4):75. 10.3390/fire4040075.
  • Kim, B., Lee. J. 2019. A video-based fire detection using deep learning models. Applied Sciences, 9(14):2862. 10.3390/app9142862.
  • Kukuk, S.B., Kilimci, Z.H. 2021. Comprehensive analysis of forest fire detection using deep learning models and conventional machine learning algorithms. International Journal of Computational and Experimental Science and Engineering, 7(2):84-4. 10.22399/ijcesen.950045.
  • Labed, S., Touati, H., Dif., R. 2022. Plant recognition using data augmentation and convolutional neural network. In International Symposium on Modelling and Implementation of Complex Systems, 192–204. Springer. 10.1007/978-3-031-18516-8_14.
  • Li, Z., Jiang, H., Mei, Q., Li, Z. 2022. Forest fire recognition based on lightweight convolutional neural network. Journal of Internet Technology, 23(5):1147–1154.
  • Madhwaraj, K.G., Asha, V., Vignesh, A., Akshay, S. 2023. Forest fire detection using machine learning. In 2023 IEEE 12th International Conference on Communication Systems and Network Technologies (CSNT), 191–196. IEEE. 10.1109/CSNT57126. 2023.10134684.
  • Mohammed, R.K. 2022. A real-time forest fire and smoke detection system using deep learning. International Journal of Nonlinear Analysis and Applications, 13(1):2053–2063.
  • Pan, H., Badawi, D., Zhang, X., Cetin, A.E. 2020. Additive neural network for forest fire detection. Signal, Image and Video Processing, 14:675–682. 10.1007/s11760-019-01600-7.
  • Pranamurti, H., Murti, A., Setianingsih, C. 2019. Fire detection use CCTV with image processing based raspberry pi. In Journal of Physics: Conference Series, volume 1201, page 012015. IOP Publishing.
  • Regi, M., Varghese, R.G., Sidharth, V. 2018. Deep learning based fire detection system. International Journal of Knowledge Based Computer Systems, 76(1):18.
  • Saeed, F., Paul, A., Karthigaikumar, P., Nayyar, A. 2020. Convolutional neural network based early fire detection. Multimedia Tools and Applications, 79:9083–9099. 10.1007/s11042-019-07785-w.
  • Sairi, A., Labed, S., Miles, B., Kout, A. 2023. A review on early forest fire detection using IoT-enabled WSN. In 2023 International Conference on Advances in Electronics, Control and Communication Systems, pp 1–6. IEEE.
  • Shambhu, S., Koundal, D., Das, P., Sharma, C. 2021. Binary classification of covid-19 ct images using cnn: Covid diagnosis using ct. International Journal of E-Health and Medical Communications (IJEHMC), 13(2):1–13.
There are 20 citations in total.

Details

Primary Language English
Subjects Photogrammetry and Remote Sensing, Forestry Sciences (Other)
Journal Section Research Articles
Authors

Said Labed 0000-0001-9273-9790

Hamza Touati 0000-0002-5151-5683

Amani Herida 0009-0000-4702-6134

Sarra Kerbab 0009-0001-9136-2546

Amira Sairi 0009-0004-9716-725X

Early Pub Date December 11, 2023
Publication Date December 26, 2023
Published in Issue Year 2023

Cite

APA Labed, S., Touati, H., Herida, A., Kerbab, S., et al. (2023). An AI-based Image Recognition System for Early Detection of Forest and Field Fires. European Journal of Forest Engineering, 9(2), 48-56. https://doi.org/10.33904/ejfe.1322396

Creative Commons License

The works published in European Journal of Forest Engineering (EJFE) are licensed under a  Creative Commons Attribution-NonCommercial 4.0 International License.