Computer Vision Based Industrial and Forest Fire Detection Using Support Vector Machine (SVM)

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IIUC Central Library


Burning issue is a very serious issue nowadays in our garments and industries sector. The workers are facing the problem and losing valuable life. On the other hand, investors are losing their hope in this sector. In this paper, we have propounded a vision-based system which is capable to detect fire. We have developed a pipeline model consisting of Background Subtraction, Color Segmentation, Special Wavelet Analysis & a Support Vector Machine which will detect real-time fire and smoke.For SVM model we have trained the dataset in two ways. One is the different kind of fire image and other is the image that looks like fire but it’s not fire. If the situation is breaking out of fire then the system will immediately raise an alarm and an automatic SMS and email will be sent to the authority and nearby fire station. In this study, the proposed strategy works on a very large dataset of fire videos that have been collected both in real life situations and from the internet.In this SVM pipeline model shows the maximum accuracy is is 93.33%. The system can fulfill the precision and detect faster real-time fire detection. Its industrial application will aid in the early detection of fires, as well as emergency management, and so greatly contribute to loss prevention.




Computer Vision, Support Vector Machine (SVM), Machine Learning, Signal Processing, Smoke & Fire Detection, Background Subtraction, Color Segmentation, Spatial Wavelet Analysis