Indoor Fire and Smoke Detection using Soft-Voting based Deep Ensemble Model

Published in:
2024 IEEE 13th International Conference on Communication Systems and Network Technologies (CSNT 2024)

DOI:
10.1109/CSNT60213.2024.10545933

Link to Paper:
IEEE Xplore Link

Code:
The code will be made available soon.

BibTeX Citation:

@inproceedings{dewangan_indoor_2024,
	title = {Indoor {Fire} and {Smoke} {Detection} {Using} {Soft}-{Voting} {Based} {Deep} {Ensemble} {Model}},
	url = {https://ieeexplore.ieee.org/abstract/document/10545933},
	doi = {10.1109/CSNT60213.2024.10545933},
	abstract = {Fire and smoke detection using vision-based technology plays a crucial role in terms of safety for indoor environments. In the literature, there are several deep learning-based fire detection solutions available, but most of the existing solutions suffer from low accuracy, high false alarm rates, and vanishing gradient issues. To overcome these issues, this paper proposed a soft-voting based deep ensemble model for fire and smoke detection tasks in which four transfer learning models such as MobileNetV2, ResNet50V2, EfficientNetB0, and DenseNet121 are used as base learners. The proposed model has a 99.11\% accuracy rate, a 97\% precision rate, a 98\% recall rate, and a 98\% F1-score.},
	urldate = {2025-05-23},
	booktitle = {2024 {IEEE} 13th {International} {Conference} on {Communication} {Systems} and {Network} {Technologies} ({CSNT})},
	author = {Dewangan, Devendra Kumar and Gupta, Govind P.},
	month = apr,
	year = {2024},
	note = {ISSN: 2473-5655},
	keywords = {Communication systems, Deep Learning, Fire Detection, Smoke Detection, Indoor environment, Market research, Safety, Smart cameras, Smart cities, Soft-Voting based Ensemble, Transfer learning, Transfer Learning},
	pages = {692--697},
}