What is it about?
Aiming at the demand for smoke and flame detection in natural scenes, this paper pro-poses a lightweight deep learning model, SF-YOLO (Smoke and Fire-YOLO), for video smoke and flame detection in such environments.
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Why is it important?
（1）To address the limitations of traditional methods in detecting targets with significant morphological alterations, ambiguous targets with unclear boundaries, and occluded targets in dynamic backgrounds, this paper proposes a lightweight deep learning model, SF-YOLO, for detecting smoke and flame in natural scenes. （2）The authors produced the smoke and fire dataset S-Firedata (Smoke and Fire data), which contains 240 images from the publicly available BoWFireDataset , the D-Fire dataset, which contains 6,860 images; the Flame fire dataset, which contains 802 images; the fire-8 flame dataset, which contains 919 images; and the video framing homemade data, which contains 1,820 images, for a total of 10,641 images.
Perspectives
（1）Our SF-YOLO model enhances the response of unobscured regions, compensates for feature loss in occluded regions, and addresses occlusion issues in dynamic backgrounds. （2）The authors published a kind of international open-access dataset the S-Firedatafor smoke and fire detection, which is an important labelled data source for deep learning based object detection research.
Dr Ka Zhang
Nanjing Normal University
Read the Original
This page is a summary of: Smoke and Fire-You Only Look Once: A Lightweight Deep Learning Model for Video Smoke and Flame Detection in Natural Scenes, Fire, March 2025, MDPI AG,
DOI: 10.3390/fire8030104.
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