Image Processing Color Model Techniques and Sensor Networking in Identifying Fire from Video Sensor Node

An early warning is an extremely important to reduce loss of life and property from fire. The region of interest is captured using CCD camera and identified by smoke sensor in the wireless sensor node. The color information of interesting region can be obtained with an application of the digital image processing color model algorithms. The fire source is identified according to the acquired characteristics and smoke level. The system is based on the continuous image sampling. The experimental results show that the system can accurately identify and confirm the fire. The video sensor node is designed with the sensors such as MQ2 sensor for smoke sensing, SHT75 sensor for temperature and humidity sensing, OPT101 sensor for light sensing and CCD camera. Alarm is activated only for fire image and fire incidents. By combining sensor output with image output, the false alarm rate is zero and improves the stability. Light detection and analysis is the basis for the fire detection system in this image processing work. In this fire image work color models such as RGB, YMK and HSI are used to separate orange, yellow, and high brightness light from background within given conditions to detect fire. Frame difference is used to analyze and calculate the growth and spread of fire. The accuracy of the system is checked and compared with one another. The amount of data processing can be reduced because of the use of proposed algorithm and thus shorten the execution time and storage.