期刊
IEEE ACCESS
卷 9, 期 -, 页码 63933-63942出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3075731
关键词
Image segmentation; Feature extraction; Sensors; Image color analysis; Fires; Temperature sensors; Convolutional neural networks; Smoke detection; SLIC; DBSCAN; convolutional neural network; super-pixel segmentation
资金
- Zhejiang Provincial Key Research and Development Project [2020C03096]
- Zhejiang Provincial Science Foundation [GF19F020010]
This study presents a method based on SLIC-DBSCAN and convolutional neural network for recognizing flame and smoke modes connected to fire stages. Experimental results demonstrate improved smoke detection capabilities with this method.
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.
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