ArticleSensors (Basel, Switzerland)2023
Transformer-Based Fire Detection in Videos.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 21 citations in OpenAlex.
- MSS-MambaNet: A Mamba Framework for Building Extraction from Multi-Phase Disaster Imagery.Sensors (Basel, Switzerland) · 2026Article
- Cross-Domain Fire Detection Across Indoor and Outdoor Scenes.Sensors (Basel, Switzerland) · 2026Article
- A Smart Visual Sensor for Smoke Detection Based on Deep Neural Networks.Sensors (Basel, Switzerland) · 2024Article
- FSH-DETR: An Efficient End-to-End Fire Smoke and Human Detection Based on a Deformable DEtection TRansformer (DETR).Sensors (Basel, Switzerland) · 2024Article
- Sensor-Based Indoor Fire Forecasting Using Transformer Encoder.Sensors (Basel, Switzerland) · 2024Article
- Welding Spark Detection on Construction Sites Using Contour Detection with Automatic Parameter Tuning and Deep-Learning-Based Filters.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Fire detection in videos forms a valuable feature in surveillance systems, as its utilization can prevent hazardous situations. The combination of an accurate and fast model is necessary for the effective confrontation of this significant task. In this work, a transformer-based network for the detection of fire in videos is proposed. It is an encoder-decoder architecture that consumes the current frame that is under examination, in order to compute attention scores. These scores denote which parts of the input frame are more relevant for the expected fire detection output. The model is capable of recognizing fire in video frames and specifying its exact location in the image plane in real-time, as can be seen in the experimental results, in the form of segmentation mask. The proposed methodology has been trained and evaluated for two computer vision tasks, the full-frame classification task (fire/no fire in frames) and the fire localization task. In comparison with the state-of-the-art models, the proposed method achieves outstanding results in both tasks, with 97% accuracy, 20.4 fps processing time, 0.02 false positive rate for fire localization, and 97% for f-score and recall metrics in the full-frame classification task.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.