Fire Detection using OpenCV with Raspberry Pi – Fire detects using image processing. Here in this project, I’m using open CV and python for fire detection. I created a HAAR Cascade Classifier for fire detection using Open CV. It has a trainer and detector for training our own cascade classifier, HAAR Cascade is used to detect objects for which it has been trained. Lots of positive and negative image samples are needed to train the classifier. Training of cascade classifier is a complex and time-consuming process. Fire Detection Using Deep Learning
With the development of the economy, the number of large high buildings is increasing. Generally, for the complex application, the high load of fire and intensive staff, major property damage, and heavy casualties will be easily caused if a fire happens in these places, and has a bad social impact. So difficult technical problems of fire detection and alarm are urgently be solved to obtain more valuable time for extinguishing and evacuation. In large rooms and high buildings, conventional fire detectors can hardly detect characteristic parameters of fire like smoke, temperature, vapor, and flame in the very early time of the fire, and cannot meet the demand of early fire detection in these places. Compared to conventional fire detectors, video fire detectors which have many advantages, such as fast response, long distance of detection, large protection area et al, are particularly applicable to large rooms and high buildings. But most of the current methods for video fire detection have high rates of false alarms. Researchers all over the world have done a lot of work on this new technique. Fire Detection Using Deep Learning
- ARDUINO CONTROLLER
- GAS SENSOR
- More cost is there to implement
- Hardware connection may loose sometimes
- Python libraries
- Background subtraction method
- No need for any hardware
- Sound alert by pc
- Military application
- Home appliances application
- Shopping malls are heavily loaded with godowns
- PYTHON IDLE
- OPEN CV MODULES
- WINDOWS OS PC
- MINIMUM 2GB RAM
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