RT Journal Article
T1 DeepSmoke: 												Deep learning model for smoke detection and segmentation in outdoor environments
A1 Khan, Salman
A1 Muhammad, Khan
A1 Hussain, Tanveer
A1 Ser, Javier Del
A1 Cuzzolin, Fabio
A1 Bhattacharyya, Siddhartha
A1 Akhtar, Zahid
A1 de Albuquerque, Victor Hugo C.
AB Fire disaster throughout the globe causes social, environmental, and economical damage, making its early detection and instant reporting essential for saving human lives and properties. Smoke detection plays a key role in early fire detection but majority of the existing methods are limited to either indoor or outdoor surveillance environments, with poor performance for hazy scenarios. In this paper, we present a Convolutional Neural Network (CNN)-based smoke detection and segmentation framework for both clear and hazy environments. Unlike existing methods, we employ an efficient CNN architecture, termed EfficientNet, for smoke detection with better accuracy. We also segment the smoke regions using DeepLabv3+, which is supported by effective encoders and decoders along with a pixel-wise classifier for optimum localization. Our smoke detection results evince a noticeable gain up to 3% in accuracy and a decrease of 0.46% in False Alarm Rate (FAR), while segmentation reports a significant increase of 2% and 1% in global accuracy and mean Intersection over Union (IoU) scores, respectively. This makes our method a best fit for smoke detection and segmentation in real-world surveillance settings.
SN 0957-4174
YR 2021
FD 2021-11-15
LA eng
NO Khan , S , Muhammad , K , Hussain , T , Ser , J D , Cuzzolin , F , Bhattacharyya , S , Akhtar , Z & de Albuquerque , V H C 2021 , ' DeepSmoke : Deep learning model for smoke detection and segmentation in outdoor environments ' , Expert Systems with Applications , vol. 182 , 115125 . https://doi.org/10.1016/j.eswa.2021.115125
NO Publisher Copyright: © 2021 Elsevier Ltd
NO The work of Salman Khan and Fabio Cuzzolin has received funding from the European Union's Horizon 2020 research and innovation programme, under grant agreement No. 964505 (E-pi). J. Del Ser acknowledges funding support from the Basque Government through the ELKARTEK program (3KIA project, KK-2020/00049) and the consolidated research group MATHMODE (ref. T1294-19). The work of Salman Khan and Fabio Cuzzolin has received funding from the European Union’s Horizon 2020 research and innovation programme, under grant agreement No. 964505 (E-pi). J. Del Ser acknowledges funding support from the Basque Government through the ELKARTEK program (3KIA project, KK-2020/00049) and the consolidated research group MATHMODE (ref. T1294-19).
DS TECNALIA Publications
RD 28 sept 2024