Experimental Study of HSV Threshold Method and U-Net Neural Net-work in Fire Recognition Task
https://doi.org/10.26907/1562-5419-2025-28-4-829-951
Abstract
A comparative analysis of image segmentation methods for fire detection was conducted using thresholding in the HSV color space and the U-Net neural network. The study aimed to evaluate the efficiency of these approaches in terms of execution time and fire detection accuracy based on RMSE, IoU, Dice, and MAPE metrics. Experiments were performed on four different fire images with manually prepared ground truth fire masks. The results showed that the HSV method offers high processing speed (0.0010–0.0020 s) but tends to detect not only fire but also smoke, reducing its accuracy (IoU 0.0863–0.3357, Dice 0.1588–0.5026). The U-Net neural network demonstrates higher fire segmentation accuracy (IoU up to 0.6015, Dice up to 0.7512) due to selective flame detection but requires significantly more time (1.2477–1.3733 s) and may underestimate the total fire area (MAPE up to 78.5840%). Visual assessment confirmed differences in methods' behavior: HSV captures smoke as part of the target area, while U-Net focuses exclusively on fire. The choice between methods depends on task priorities: speed or accuracy. Future research directions were proposed, including U-Net optimization and the development of hybrid approaches.
About the Authors
Maksim Vladimirovich Bobyr
Southwest State University
Russian Federation
Russian Federation
Natalya Anatolyevna Milostnaya
Southwest State University
Russian Federation
Russian Federation
Bogdan Andreevich Bondarenko
South-West State University
Russian Federation
Russian Federation
Maksim Maksimovich Bobyr
Southwest State University
Russian Federation
Russian Federation
References
1. Waldamichael F.G., Debelee T.G., Ayano Y.M. Coffee disease detection using a robust HSV color-based segmentation and transfer learning for use on smartphones // International Journal of Intelligent Systems/ 2022. Vol. 37, No. 8. P. 4967–4993. https://doi.org/10.1002/int.22747
2. Pratomo A.H., Kaswidjanti W., Nugroho A.S., Saifullah S. Parking detection system using background subtraction and hsv color segmentation // Bulletin of Electrical Engineering and Informatics. 2021. Vol. 10, No. 6. P. 3211–3219.
3. https://doi.org/10.11591/eei.v10i6.3251
4. Wang Y., Han Q., Li Y., Li Y. Video smoke detection based on multi-feature fusion and modified random forest // Engineering Letters. 2021. Vol. 29, No. 3. P. 1115–1122.
5. Kokovkina V.A., Antipov V.A. Adaptivnaya segmentatsiya simvolov na avtomobil'nykh nomerakh // DSPA: Voprosy primeneniya tsifrovoy obrabotki signalov. 2016. T. 6, № 3. S. 663–666.
6. Li Y., Ge M., Zhang S., Wang K. Adaptive Segmentation Algorithm for Subtle Defect Images on the Surface of Magnetic Ring Using 2D-Gabor Filter Bank // Sensors. 2024. Vol. 24, No. 3. https://doi.org/10.3390/s24031031
7. Zhuykova Ye.G. Sravnitel'nyy analiz adaptivnogo metoda k-srednikh i porogovoy klasterizatsii // Perspektivy nauki. 2024. Т. 6(177). S. 92–98.
8. Rezki M., Nurdiani S., Safitri R.A., Ihsan M.I.R. Iqbal M. Segmentasi Api dan Asap Pada Kebakaran Dengan Metode K-Means Clustering // Computer Science (CO-SCIENCE). 2022. Vol. 2, No. 1. P. 26–32. https://doi.org/10.31294/coscience.v2i1.849
9. Zimichev Ye.A., Kazanskiy N.L., Serafimovich P.G. Prostranstvennaya klassifikatsiya giperspektral'nykh izobrazheniy s ispol'zovaniyem metoda kla-sterizatsii k-means++// Komp'yuternaya optika. 2014. Т. 38, № 2. S. 281–286. https://doi.org/10.18287/0134-2452-2014-38-2-281-286
10. Pereyra M., McLaughlin S. Fast Unsupervised Bayesian Image Segmentation with Adaptive Spatial Regularisation // IEEE Transactions on Image Processing. 2017. Vol. 26, No. 6. P. 2577–2587. https://doi.org/10.1109/TIP.2017.2675165
11. Singh K.R., Neethu K.P., Madhurekaa K., Harita A., Mohan P. Parallel SVM model for forest fire prediction // Soft Computing Letters. 2021. Vol. 3. 100014.
12. https://doi.org/10.1016/j.socl.2021.100014
13. Xiong D., Yan L. Early smoke detection of forest fires based on SVM image segmentation //Journal of Forest Science. 2019. Vol. 65, No. 4. P. 150–159. https://doi.org/10.17221/82/2018-JFS
14. Demin N.S., Il'yasova N.Yu., Paringer R.A., Kirsh D.V. Primeneniye iskusstvennogo intellekta v oftal'mologii na primere resheniya zadachi se-manticheskoy segmentatsii izobrazheniya glaznogo dna // Komp'yuternaya optika. 2023. Т. 47, № 5. S. 824–831. https://doi.org/10.18287/2412-6179-CO-1283
15. Gavrilov D.A. Issledovaniye primenimosti svertochnoy neyronnoy seti U-Net k zadache segmentatsii izobrazheniy aviatsionnoy tekhniki // Komp'yuternaya optika. 2021. Т. 45, № 4. S. 575–579. https://doi.org/10.18287/2412-6179-CO-804
16. Mseddi W.S., Ghali R., Jmal M., Attia R. Fire Detection and Segmentation using YOLOv5 and U-NET // In European Signal Processing Conference (Vol. 2021-August, pp. 741–745). European Signal Processing Conference, EUSIPCO. 2021. https://doi.org/10.23919/EUSIPCO54536.2021.9616026
17. Bochkov V.S., Katayeva L.Yu., Maslennikov D.A., Kasparov I.V. Primeneniye arkhitektury glubokogo obucheniya U-Net dlya resheniya zadachi vydeleniya vysokotemperaturnykh zon pozhara na video // Trudy NGTU im. R.Ye. Alekseyeva. 2019. Т. 3 (126). S. 9–16. https://doi.org/10.46960/1816-210X_2019_3_9
18. Bochkov V.S., Katayeva L.Yu., Maslennikov D.A. Tochnaya mnogoklasso-vaya segmentatsiya pozharov: podkhody, neyronnyye seti, skhemy segmentatsii // Iskusstvennyy intellekt i prinyatiye resheniy. 2024. Т. 3. S. 71–86. https://doi.org/10.14357/20718594240306
19. Barmpoutis P., Stathaki T., Dimitropoulos K., Grammalidis N. Early fire detection based on aerial 360-degree sensors, deep convolution neural networks and exploitation of fire dynamic textures // Remote Sensing, 2020. Vol. 12 (19). P. 1–17. https://doi.org/10.3390/rs12193177
20. Panina V.S., Amelichev G.E. Primeneniye svertochnykh neyronnykh setey Mask R-CNN v intellektual'nykh parkovochnykh sistemakh // E-Scio. 2022. Т. 6(69). S. 425–432.
21. Begum S.R., S Y.D., M S.V.M. Mask R-CNN for fire detection // International Research Journal of Computer Science. 2021. Vol. 8, No. 7. P. 145–151. https://doi.org/10.26562/irjcs.2021.v0807.003
22. Zhou Y.C., Hu Z.Z., Yan K.X., Lin J.R. Deep Learning-Based Instance Segmentation for Indoor Fire Load Recognition // IEEE Access. 2021. Vol. 9. P. 148771–148782. https://doi.org/10.1109/ACCESS.2021.3124831
23. Tsalera E., Papadakis A., Voyiatzis I., Samarakou M. CNN-based, contextualized, real-time fire detection in computational resource-constrained environments // Energy Reports. 2023. Vol. 9. P. 247–257. https://doi.org/10.1016/j.egyr.2023.05.260
24. Nguyen T.H., Nguyen T.N., Ngo B.V. A VGG-19 Model with Transfer Learning and Image Segmentation for Classification of Tomato Leaf Disease // AgriEngineering. 2022. Vol. 4, No. 4. P. 871–887. https://doi.org/10.3390/agriengineering4040056
25. Almeida J.S., Huang C., Nogueira F.G., Bhatia S., De Albuquerque V.H.C. EdgeFireSmoke: A Novel Lightweight CNN Model for Real-Time Video Fire-Smoke Detection // IEEE Transactions on Industrial Informatics. 2022. Vol. 18, No. 11. P. 7889–7898. https://doi.org/10.1109/TII.2021.3138752
26. Jeong S.W., Yoo J. I-firenet: A lightweight CNN to increase generalization performance for real-time detection of forest fire in edge AI environments // Journal of Institute of Control, Robotics and Systems. 2020. Vol. 26, No. 9. P. 802–810. https://doi.org/10.5302/J.ICROS.2020.20.0033
27. Nadeem M., Dilshad N., Alghamdi N.S., Dang L.M., Song H.K., Nam J., Moon H. Visual Intelligence in Smart Cities: A Lightweight Deep Learning Model for Fire Detection in an IoT Environment // Smart Cities. 2023. Vol. 6, No. 5. P. 2245–2259. https://doi.org/10.3390/smartcities6050103
28. Ryu J., Kwak D. Flame detection using appearance‐based pre‐processing and convolutional neural network // Applied Sciences (Switzerland). 2021. Vol. 11, No. 11. https://doi.org/10.3390/app11115138
29. Roh J.-H., Min S.-H., Kong M. Flame Segmentation Characteristics of YCbCr Color Model Using Object Detection Technique // Fire Science and Engineering. 2023. Vol. 37, No. 6. P. 54–61. https://doi.org/10.7731/kifse.7c1d5c35
30. Wang X., Li M., Gao M., Liu Q., Li Z., Kou L. Early smoke and flame detection based on transformer // Journal of Safety Science and Resilience. 2023. Vol. 4, No. 3. P. 294–304. https://doi.org/10.1016/j.jnlssr.2023.06.002
31. Bobyr M., Arkhipov A., Emelyanov S., Milostnaya N. A method for creating a depth map based on a three-level fuzzy model // Engineering Applications of Artificial Intelligence. 2023. Vol. 117. https://doi.org/10.1016/j.engappai.2022.105629
Review
For citations:
Bobyr M.V., Milostnaya N.A., Bondarenko B.A., Bobyr M.M. Experimental Study of HSV Threshold Method and U-Net Neural Net-work in Fire Recognition Task. Russian Digital Libraries Journal. 2025;28(4):829-951. (In Russ.) https://doi.org/10.26907/1562-5419-2025-28-4-829-951
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