Efficient Flame Detection Based on Static and Dynamic Texture Analysis in Forest Fire Detection
| Autor: | S. Suresh, S. S. Vinsley, C. Emmy Prema |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Computer science
Fire detection business.industry Flame detection ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020101 civil engineering Pattern recognition YCbCr 02 engineering and technology Color space Luminance GeneralLiterature_MISCELLANEOUS 0201 civil engineering 0202 electrical engineering electronic engineering information engineering Chrominance 020201 artificial intelligence & image processing General Materials Science Artificial intelligence Safety Risk Reliability and Quality business Classifier (UML) ComputingMethodologies_COMPUTERGRAPHICS Extreme learning machine |
| Zdroj: | Fire Technology. 54:255-288 |
| ISSN: | 1572-8099 0015-2684 |
| Popis: | Flame detection is a specialized task in fire detection and forest fire monitoring systems. In this paper, a static and dynamic texture analysis of flame in forest fire detection is proposed. The flames are initially segmented, based on the color in YCbCr (luminance, chrominance blue and chrominance red components) color space called candidate flame region. From the candidate flame region, the static and dynamic texture features are extracted. Static texture features are obtained by hybrid texture descriptors. Dynamic texture features are derived using 2D wavelet decomposition in temporal domain and 3D volumetric wavelet decomposition. Finally, extreme learning machine classifier is used to classify the candidate flame region as real flame or non-flame, based on the extracted texture features. The proposed flame detection system is applied to various fire detection scenes, in the real environments and it effectively distinguishes fire from fire-colored moving objects. The results show that the proposed fire detection technique achieves the average detection rate of 95.65% which is better compared to other state-of-art methods. |
| Databáze: | OpenAIRE |
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