Research Of Fire Detection System Based On FPGA

There is a common catastrophic problem for the people all over the world that fire can damage people’s lives, so it is some great help if there is an early discovery via an accurate identification. The video image fusion based flame detection method merges image processing, computer vision, pattern recognition and digital signal processing technology. This kind of detection has a good advantage of high accuracy detection, anti-interference ability, versatility, etc. It has taken place of the traditional sense smoke, temperature, light-sensitive and infrared fire detection gradually and becomes hot in research field of fire industry in recent year.This paper proposes a new system implementation method of video flame detection based on FPGA on a condition of finishing some researches. These researches include the creative idea and implementation methods in this industry at home and abroad. This new method make full use of excellent parallel processing ability, rich internal resources and reconfigurable logic by Verilog HDL programming only. Compared with the DSP and ARM embedded processor, it has a good advantage of computing speed, real-time plan and flexibility in upgrade.This system takes EP2C35, Altera’s Cyclone Ⅱ series as the core. It is made up of six modules. They are digital camera initialization module D5M, image acquisition module, image conversion module, multi-port controller module SDRAM, interface control module VGA, flame detection and localization. In addition, flame detection and localization contains a color space conversion, flame recognition module and flame localization module. In flame detection algorithm, the judgment conditions of flame area identification are the color characteristics of discrimination and the flame target motion characteristics. The purpose of this system is to realize flame identification and detection result shown on a VGA monitor instantly.The development of this system is based on Altera hardware DE2platform and QuartusII software development environment. It has a great real-time ability and reliability, because there is no Nios II soft-core in video image acquisition and control, of the implementation of the lame detection algorithm. Finally, it is shown the performance of he flame video image detection and analysis of the experimental results and system hardware resource consumption.