Russian Federation
Russian Federation
Russian Federation
The study conducted a comparative analysis of domestically produced unmanned aerial systems for detecting and monitoring forest fires. It was noted that the domestic market is currently actively developing, and Russian models are comparable to their foreign counterparts. The study focused on domestically produced aircraft-type unmanned aerial vehicles, namely: SIGMA, Patrol-30, Gorizont-Aero, Skat, Grusha, Supercam S-250, InnoVtol-3s, Zala 421-04M and Orlan-10. The study's methodology included a critical analysis of these models. Each model was examined in detail, and the best were identified based on flight range, maximum flight time, weight, dimensions, payload, communication range, and cost. The distinctive features of each model were also considered. The data obtained demonstrate the significant potential of using domestically produced unmanned aerial systems for detecting and monitoring forest fires. Aircraft-based models demonstrate the greatest effectiveness.
analysis, comparative analysis, unmanned aerial vehicles, unmanned aerial vehicle, unmanned aerial systems, domestic production, monitoring, detection, forest fires
1. Asadov H.G., Bajramov G.Z. Voprosy sozdaniya sistemy rannego obnaruzheniya lesnyh pozharov na baze bespilotnyh aviacionnyh sistem // Kontrol'. Diagnostika. 2024. T. 27. № 4 (310). S. 64–68. DOI:https://doi.org/10.14489/td.2024.04.pp.064-068. EDN SCMPEK.
2. Sysoeva T.P., Kalach A.V. Perspektivy razvitiya bespilotnyh aviacionnyh sistem // Sibirskij pozharno-spasatel'nyj vestnik. 2025. № 3 (38). S. 111–118. DOI:https://doi.org/10.34987/vestnik.sibpsa.2025.41.36.010.
3. Nasyrova G.N., Nasyrov I.R. Obnaruzhenie lesnyh pozharov s pomoshch'yu BPLA samoletnogo tipa // Mezhdunarodnyj zhurnal gumanitarnyh i estestvennyh nauk. 2024. № 9-5 (96). S. 7–11. DOI:https://doi.org/10.24412/2500-1000-2024-9-5-7-11. EDN MHUYQD.
4. Tipy BPLA i vozmozhnosti ispol'zovaniya v celyah monitoringa i predotvrashcheniya lesnyh pozharov / A.E. Serebryakov [i dr.] // Nauka. Tekhnika. Tekhnologii (politekhnicheskij vestnik). 2021. № 4. S. 175–178. EDN LBFWSB.
5. Igajkina I.I., Das'kin I.N. Analiz effektivnosti bespilotnyh aviacionnyh sistem dlya monitoringa pozharov sel'hozugodij // Sel'skij mekhanizator. 2023. № 1-2. S. 5–7. DOI:https://doi.org/10.47336/0131-7393-2023-1-2-5-6-7. EDN RKLEFV.
6. Yakovenko T.A., Sopiga V.A. Avtomaticheskij monitoring i izmerenie pozharov s ispol'zovaniem bespilotnyh aviacionnyh sistem // Innovacii i investicii. 2025. № 3. S. 529–532. EDN XOYZSU.
7. Korolev D.S., Kalach A.V., Konchakov S.A. Sovershenstvovanie tekhnicheskih intellektual'nyh sistem obnaruzheniya i monitoringa lesnyh pozharov // Problemy upravleniya riskami v tekhnosfere. 2023. № 1 (65). S. 105–113. EDN UESSTV.
8. Veretennikova N.S., Kislov V.I., Eremenko K.Yu. Problema svoevremennogo obnaruzheniya i likvidaciya lesnyh pozharov // Byulleten' nauki i praktiki. 2021. T. 7. № 6. S. 56–59. DOI:https://doi.org/10.33619/2414-2948/67/07. EDN IHUZJP.
9. Kataev M.Yu., Kartashov E.Yu., Gejko P.P. Obnaruzhenie lesnyh pozharov po izobrazheniyam, poluchennym s BPLA // Doklady Tomskogo gosudarstvennogo universiteta sistem upravleniya i radioelektroniki. 2023. T. 26. № 3. S. 72–79. DOI:https://doi.org/10.21293/1818-0442-2023-26-3-72-79. EDN EDMOMX.
10. Belyaev A.E., Budevich E.A., Vycherova N.R. Vydelenie plameni i dyma na izobrazheniyah, poluchennyh kamerami BPLA v sisteme rannego obnaruzheniya lesnyh pozharov // Sistemy. Metody. Tekhnologii. 2022. № 4 (56). S. 126–131. DOI:https://doi.org/10.18324/2077-5415-2022-4-126-131. EDN CPNSWY.
11. Georgiev A.G. An evaluation of fire detection methods: comparative analysis and performance assessment 16 // Proceedings of University of RUSE. 2023. T. 62.
12. Akhloufi M.A., Couturier A., Castro N.A. Unmanned aerial vehicles for wildland fires: Sensing, perception, cooperation and assistance // Drones. 2021. T. 5. № 1. S. 15.
13. Unmanned aerial vehicle assisted forest fire detection using deep convolutional neural network / A. Rahman [et al.] // Intell. Autom. Soft Comput. 2023. T. 35. № 3. S. 3259–3277.
14. UAVs-FFDB: A high-resolution dataset for advancing forest fire detection and monitoring using unmanned aerial vehicles (UAVs) / M.N. Mowla [et al.] // Data in brief. 2024. T. 55. S. 110706.
15. Unmanned aerial vehicle-based forest fire detection systems: A comprehensive review / J. Patel [et al.] // Available at SSRN 4603404. 2023.
16. Intelligent methods for forest fire detection using unmanned aerial vehicles / N. Abramov [et al.] // Fire. 2024. T. 7. № 3. S. 89.
17. Forest fire monitoring system supported by unmanned aerial vehicles and edge computing: a performance evaluation using petri nets / A. Sabino [et al.] // Cluster Computing. 2024. T. 27. № 7. S. 9735–9755.
18. The use of unmanned aerial vehicles in the detection of forest fires with a gas detection technique / M. Masat [et al.] // NanoEra. 2021. T. 1. № 1. S. 14–18.
19. Recent advances in unmanned aerial vehicle forest remote sensing – A systematic review. part I: A general framework / R. Dainelli [et al.] // Forests. 2021. T. 12. № 3. S. 327.
20. Data collection task planning of a fixed-wing unmanned aerial vehicle in forest fire monitoring / H. Zhang [et al.] // IEEE Access. 2021. T. 9. S. 109847–109864.
21. Kim S.Yu., Muminov A. Forest fire smoke detection based on deep learning approaches and unmanned aerial vehicle images // Sensors. 2023. T. 23. № 12. S. 5702.
22. Sarikaya Basturk N. Forest fire detection in aerial vehicle videos using a deep ensemble neural network model // Aircraft engineering and aerospace technology. 2023. T. 95. № 8. S. 1257–1267.
23. Potential of UAV application for forest fire detection / A. Muid [et al.] // Journal of Physics: Conference Series. IOP Publishing, 2022. T. 2243. № 1. S. 012041.
24. Sharma A., Singh P. K. UAV based framework for effective data analysis of forest fire detection using 5G networks: An effective approach towards smart cities solutions // International Journal of Communication Systems. 2025. T. 38. № 1. S. e4826.
25. Recent advances in Unmanned Aerial Vehicles forest remote sensing – A systematic review. Part II: Research applications / R. Dainelli [et al.] // Forests. 2021. T. 12. № 4. S. 397.
26. A vision-based detection and spatial localization scheme for forest fire inspection from UAV / K. Lu [et al.] // Forests. 2022. T. 13. № 3. S. 383.
27. Ispol'zovanie bespilotnyh aviacionnyh sistem dlya obnaruzheniya lesnyh pozharov / M.V. Polezhaeva [i dr.] // Informacionnye tekhnologii i sistemy: upravlenie, ekonomika, transport, pravo. 2024. № 1 (49). S. 67–78. EDN KKRFTC.
