Fire Detection using OpenCV with Raspberry Pi

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Description

Fire Detection

ABSTRACT:

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

INTRODUCTION

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


EXISTING METHOD

  • ARDUINO CONTROLLER
  • GAS SENSOR

DISADVANTAGES

  • More cost is there to implement
  • Hardware connection may loose sometimes

PROPOSED METHOD:

  • Python libraries
  • Background subtraction method

ADVANTAGES

  • No need for any hardware
  • Sound alert by pc 

APPLICATIONS

  • Military application
  • Home appliances application
  • Shopping malls are heavily loaded with godowns

BLOCK DIAGRAM

Fire Detection
Fire Detection

SOFTWARE REQUIREMENTS:

  • PYTHON IDLE
  • OPEN CV MODULES

HARDWARE REQUIREMENTS:

  • WINDOWS OS PC 
  • MINIMUM 2GB RAM

 


REFERENCE

[1] D. Han and B. Lee, “Development of early tunnel fire detection algorithm using the image processing,” in International Symposium on Visual Computing, 2006, pp. 39-48. 

[2] T.-H. Chen, P.-H. Wu, and Y.-C. Chiou, “An early fire-detection method based on image processing,” in Image Processing, 2004. ICIP’04. 2004 International Conference on, 2004, pp. 1707-1710. 

[3] G. Marbach, M. Loepfe, and T. Brupbacher, “An image processing technique for fire detection in video images,” Fire safety journal, vol. 41, pp. 285-289, 2006.

[4] T. Celik and H. Demirel, “Fire detection in video sequences using a generic color model,” Fire Safety Journal, vol. 44, pp. 147-158, 2009. 

[5] A. Rafiee, R. Dianat, M. Jamshidi, R. Tavakoli, and S. Abbaspour, “Fire and smoke detection using wavelet analysis and disorder characteristics,” in Computer Research and Development (ICCRD), 2011 3rd International Conference on, 2011, pp. 262-265. 

[6] Y. H. Habiboğlu, O. Günay, and A. E. Çetin, “Covariance matrixbased fire and flame detection method in video,” Machine Vision and Applications, vol. 23, pp. 1103-1113, 2012. 

[7] R. Di Lascio, A. Greco, A. Saggese, and M. Vento, “Improving fire detection reliability by a combination of video analytics,” in International Conference Image Analysis and Recognition, 2014, pp. 477-484

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