Version 1
: Received: 1 April 2020 / Approved: 3 April 2020 / Online: 3 April 2020 (04:43:58 CEST)
How to cite:
Zhao, Y.; zhang, H.; Zhang, X.; Qian, W. Wildfire Smoke Detection Based on Depthwise Separable Convolutions and Target-Awareness. Preprints2020, 2020040027. https://doi.org/10.20944/preprints202004.0027.v1
Zhao, Y.; zhang, H.; Zhang, X.; Qian, W. Wildfire Smoke Detection Based on Depthwise Separable Convolutions and Target-Awareness. Preprints 2020, 2020040027. https://doi.org/10.20944/preprints202004.0027.v1
Zhao, Y.; zhang, H.; Zhang, X.; Qian, W. Wildfire Smoke Detection Based on Depthwise Separable Convolutions and Target-Awareness. Preprints2020, 2020040027. https://doi.org/10.20944/preprints202004.0027.v1
APA Style
Zhao, Y., zhang, H., Zhang, X., & Qian, W. (2020). Wildfire Smoke Detection Based on Depthwise Separable Convolutions and Target-Awareness. Preprints. https://doi.org/10.20944/preprints202004.0027.v1
Chicago/Turabian Style
Zhao, Y., Xinliang Zhang and Wei Qian. 2020 "Wildfire Smoke Detection Based on Depthwise Separable Convolutions and Target-Awareness" Preprints. https://doi.org/10.20944/preprints202004.0027.v1
Abstract
Since smoke usually occurs before a flame arises, fire smoke detection is especially significant for early warning systems. In this paper, a DSATA(Depthwise Separability And Target Awareness) algorithm based on depthwise separability and target awareness is proposed. Existing deep learning methods with convolutional neural networks pretrained by abundant and vast datasets are always used to realize generic object recognition tasks. In the area of smoke detection, collecting large quantities of smoke data is a challenging task for small sample smoke objects. The basis is that the objects of interest can be arbitrary object classes with arbitrary forms. Thus, deep feature maps acquired by target-aware pretrained networks are used in modelling these objects of arbitrary forms to distinguish them from unpredictable and complex environments. In this paper, this scheme is introduced to deal with smoke detection. The depthwise separable method with a fixed convolution kernel replacing the training iterations can improve the speed of the algorithm to meet the enhanced requirements of real-time fire spreading for detecting speed. The experimental results demonstrate that the proposed algorithm can detect early smoke, is superior to the state-of-the-art methods in accuracy and speed, and can also realize real-time smoke detection.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
