Enhancement of early fire detection using improved YOLOv8-based visual smoke detection model
Fragment książki (Rozdział monografii pokonferencyjnej)
MNiSW
20
Poziom I
| Status: | |
| Autorzy: | Kvyetnyy Roman N., Smolarz Andrzej, Maslii Roman, Kabachii Vladyslav, Kozbakova Ainur, Harmash Volodymyr, Savina Nataliia B., Shvarts Iryna V., Ussipbekova Dinara |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Strony: | 1 - 7 |
| Scopus® Cytowania: | 0 |
| Bazy: | Scopus |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | TAK |
| Nazwa konferencji: | Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2025 |
| Skrócona nazwa konferencji: | SPIE-IEEE-PSP 2025 |
| URL serii konferencji: | LINK |
| Termin konferencji: | 3 lipca 2025 do 4 lipca 2025 |
| Miasto konferencji: | Lublin |
| Państwo konferencji: | POLSKA |
| Publikacja OA: | NIE |
| Abstrakty: | angielski |
| This paper proposes modifications to the YOLOv8 architecture for visual smoke detection in early fire detection systems. To reduce the complexity of the architecture, it is proposed to use the VoVGSCSP block, to improve the detection quality – the efficient multi-scale attention (EMA) block. The proposed changes allowed to reduce the number of neural network parameters by 27% and the computational complexity (GFLOPS) by 12% compared to the YOLOv8n model. The proposed model retained high detection accuracy, compared to the base model, the decrease in detection accuracy by the quality assessment metrics mAP@0.5 and mAP@0.5:0.95 was about 1%. To train and evaluate the model, an own dataset of more than 5000 images was created based on the open datasets D-Fire and WSDY. The obtained results demonstrate the suitability of the model for use on edge devices as part of video surveillance systems for early fire detection. |
