Computer Vision and Smoke Sensor Based Fire Detection System

Unwanted fire causes many disasters. Thus, fire detection has been an important phenomenon to save human life. In this paper, we proposed a hybrid model that incorporates computer vision and smoke sensor-based fire detection into a single system to devise a more accurate and smart fire detection system. Our vision-based fire detection model utilizes color and motion attributes of fire combustion. Rule-based color segmentation is performed for classifying fire color regions and a modified image difference technique is used for detecting the motion of fire regions in consecutive frames. The proposed dynamic threshold technique is used for mitigating the false positive rate of the vision-based model. After performing a vision-based analysis, the proposed algorithm uses the MQ-2 smoke sensor to detect environment smoke and gas caused by fire combustion. Our implemented system raises a fire alarm in the form of remote notification after analyzing vision and smoke sensor-based model data. Real life experimental data shows that our system can detect fire with 86.67% of accuracy. In addition, the proposed system can be incorporated in existing surveillance systems at an adequately low extra expense.