Automatic smoke detection based on SLIC-DBSCAN enhanced convolutional neural network

Video flame and smoke-based fire detection usually exhibit large variations in the feature of color, texture, shapes, etc., caused by the complex environment. It is difficult to develop a robust method to detect fire based on single or multiple fire features. Since convolutional neural network (CNN)...

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Bibliographic Details
Published in:IEEE access 2021-01, Vol.9, p.1-1
Main Authors: Sheng, Dali, Deng, Jinlian, Xiang, Jiawei
Format: Article
Language:English
Subjects:
Artificial neural networks
Clustering
convolutional neural network
Convolutional neural networks
Data collection
DBSCAN
Feature extraction
Fire detection
Fires
Image color analysis
Image segmentation
Neural networks
Pixels
Sensors
SLIC
Smoke
Smoke detection
super-pixel segmentation
Temperature sensors
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Summary:Video flame and smoke-based fire detection usually exhibit large variations in the feature of color, texture, shapes, etc., caused by the complex environment. It is difficult to develop a robust method to detect fire based on single or multiple fire features. Since convolutional neural network (CNN) has reported state-of-the-art performance in a wide range of fields. This study present a method based on SLIC-DBSCAN and convolutional neural network to recognize flame and smoke modes connected to fire stages. First, simple linear iterative clustering (SLIC) is acted as the pre-processing step to over segment images into super-pixels. Then the use of density based spatial clustering of application with noise (DBSCAN) gathered the similar super-pixels into several clusters, which in turn provide better smoke detection accuracy by using CNN. Comparison studies are performed to base on smoke image from publicly available data and self-collected data. The experimental results demonstrated the improved smoke detection capabilities by the present method.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3075731