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• W3159296779 abstract "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." @default.
• W3159296779 created "2021-05-10" @default.
• W3159296779 creator A5006189098 @default.
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• W3159296779 date "2021-01-01" @default.
• W3159296779 modified "2023-10-03" @default.
• W3159296779 title "Automatic Smoke Detection Based on SLIC-DBSCAN Enhanced Convolutional Neural Network" @default.
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• W3159296779 doi "https://doi.org/10.1109/access.2021.3075731" @default.
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