Flame Recognition: Image Processing Solutions for Rapid Fire Detection
Author(s):
Ajay Bhosle, Abhinandan Ambekar, Shrikrushna Bombe, Vivek Ingale
Keywords:
Fire detection, CNN, Machine learning, Fire Detection, OpenCV, Background Subtraction, Contour Detection, Image Processing, Motion Detection
Abstract
Image processing is a technology used in fire detection that analyzes photos or video streams to detect the presence of fire using computer vision algorithms. The main features of this strategy are: In order to identify fire from other things or situations, image processing techniques for fire detection usually entail assessing visual features such color, texture, motion, and shape. When compared to conventional techniques, convolutional neural networks (CNNs) have demonstrated encouraging results in terms of increasing the accuracy of fire detection. Differentiating fire from smoke or other similar visual elements is a common problem that can result in false alarms. Some solutions to this problem combine infrared and visible imaging to improve the ability to distinguish smoke from other things. Additionally, methods like fuzzy logic and wavelet analysis have been investigated to improve To improve the resilience of fire detection, methods Even though image processing-based fire detection can cover more ground and respond more quickly than sensor-based systems, researchers are still focused on addressing the computational demands and possibility of false alarms through multi modal sensor fusion and algorithm optimization. In general, image processing for fire detection is a fast developing subject that seeks to use developments in computer vision and machine learning to increase the efficiency, speed, and affordability of fire monitoring and early warning systems.
Article Details
Unique Paper ID: 165037

Publication Volume & Issue: Volume 11, Issue 1

Page(s): 35 - 40
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