@Article{PSKarydis2026,
AUTHOR = {Polenakis, Iosif and Sarantidis, Christos and Karydis, Ioannis},
TITLE = {Benchmarking Next-Generation YOLO Architectures for Multi-Platform Forest Fire Recognition},
JOURNAL = {Electronics},
VOLUME = {15},
YEAR = {2026},
NUMBER = {13},
ARTICLE-NUMBER = {2830},
URL = {https://www.mdpi.com/2079-9292/15/13/2830},
ISSN = {2079-9292},
ABSTRACT = {Early and reliable detection of forest fires is essential for reducing environmental damage and ensuring public safety. Deep learning-based object detection enables automated fire monitoring across heterogeneous sensing platforms, including satellite, Unmanned Aerial Vehicle (UAV), and ground-based imaging systems. However, differences in spatial resolution, viewing geometry, and computational constraints present challenges for developing unified detection models. This study presents a comparative benchmarking analysis of the lightweight YOLOv26-nano model for forest fire detection using the FASDD dataset, comprising satellite, UAV, and ground-based imagery. A unified experimental protocol with five-fold cross-validation is adopted to ensure robustness and cross-platform generalization. Performance is enhanced through data augmentation, contrast-limited adaptive histogram equalization, and stochastic gradient descent optimization. Experimental results demonstrate that YOLOv26-nano achieves reliable detection accuracy and demonstrates promising computational characteristics under simulated resource-constrained edge-computing conditions. The proposed benchmarking framework provides a standardized reference for multi-platform fire detection and highlights the suitability of nano-scale object detection models for scalable wildfire monitoring and early-warning systems.},
DOI = {10.3390/electronics15132830},
keywords = {YOLO; forest fire detection; UAV; drone; satellite imagery; edge computing; multi-platform; object detection; wildfire monitoring; deep learning},
note = {https://cir.di.ionio.gr/karydis/my_papers/PSKarydis2026%20-%20Benchmarking%20Next-Generation%20YOLO%20Architectures%20for%20Multi-Platform%20Forest%20Fire%20Recognition.pdf}