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Title	Firenet: A Lightweight And Efficient Multi-Scenario Fire Object Detector
ID_Doc	26592
Authors	He Y.; Sahma A.; He X.; Wu R.; Zhang R.
Year	2024
Published	Remote Sensing, 16, 21
DOI	http://dx.doi.org/10.3390/rs16214112
Abstract	Fire and smoke detection technologies face challenges in complex and dynamic environments. Traditional detectors are vulnerable to background noise, lighting changes, and similar objects (e.g., clouds, steam, dust), leading to high false alarm rates. Additionally, they struggle with detecting small objects, limiting their effectiveness in early fire warnings and rapid responses. As real-time monitoring demands grow, traditional methods often fall short in smart city and drone applications. To address these issues, we propose FireNet, integrating a simplified Vision Transformer (RepViT) to enhance global feature learning while reducing computational overhead. Dynamic snake convolution (DSConv) captures fine boundary details of flames and smoke, especially in complex curved edges. A lightweight decoupled detection head optimizes classification and localization, ideal for high inter-class similarity and small targets. FireNet outperforms YOLOv8 on the Fire Scene dataset (FSD) with a mAP@0.5 of 80.2%, recall of 78.4%, and precision of 82.6%, with an inference time of 26.7 ms. It also excels on the FSD dataset, addressing current fire detection challenges. © 2024 by the authors.
Author Keywords	decoupled detection head; DSConv; fire and smoke detection; RepVit; YOLO

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