The Preliminary Study Of Early Agricultural And Forestry Fire Detection Method Based On Visual Features

Fire is the one of the universal threats to public safety and social development, and brings great harm to productivity in agriculture, industry and people's life. With the development of science and technology, automatic fire detection has become an effective means of fire prevention and monitoring. Vision-based early fire automatical detection technology can be more efficient and accurate to detect fires with great environmental adaptation ability. Combined with computer technology, it can provide more intuitive and abundant fire information. Compared with the vision-based fire automatical detection technol ogy, the tradi ti onal automati c fi re detecti on technology is costly and complicated to install, difficult to apply to farmland, pasture, forest fire detection and other situations of large space. As a new and intelligent method, fire detection technique based on vision will have broad application value and prospects.This thesis studies the early fire detection method based on visual characteristics of the fire. First, it describes the composition of the fire detection system, analyses the static and dynamic characteristics of the wild early fires, and analyses the procedure of the fire detection algorithm. I n order to meet the real-time requirements of the detection algorithm, thi s thesi s proposes a new i mage enhancement method to enhance the characteri sti cs of the image between the different objects for image information extraction subsequently. This thesis also proposes a method of the color image segmentation based neural network, extracts the pixels of flames and smoke images, and provides a statistical rule to discriminate flames and smoke for the subsequent analysis of pixel color. This thesis proposes a new algorithm of fast motion detection to support the subsequent processing of flame and smoke detection. At last, this thesis proposes the methods of real-time flame detection and real-time smoke detection based on visual characteristics, and completes the basic research of fire detection.The main work of this thesis as follows:1. This thesis introduces the meaning of fire detection and prevention in the agricultural. First, this thesis discourses on the importance and necessity of the automatic fire detection, compares the features of fire detection technology based on visual characteristics with the traditional fire detection technology, and then expounds the advantages of the fire technology based on visual features. Finally, this thesis analyzes the components of the automatic fire detection system based on visual features, and explained the main f uncti ons and the role of the vari ous parts of the system.2. This thesis discusses and compares the fire detection algorithms based on visual features and current situations correspondingly, proposes the algorithm processes of the fire detection based on visual features, analyses the static and dynamic visual characteri stics of the smoke and flame image, and reviews the detection algorithm of flame and smoke detection methods.3. This thesis proposes a fast algorithm of color image enhancement based on fuzzy logic algorithm in order to meat the real-time demands for the practical application. This algorithm preserves the static and dynamic characteristics of the fire image, and it processes to the pixel directly in the HSI color model with the new membership function, complete the rapid enhancement processing through the lookup table to the fire image to meet the real-time requirements.4.In order to obtai n the statistical characteri sti cs of the pixel s of f i re i mage, thi s thesi s proposed an algorithm of color image segmentation based a neural network. In this algorithm, the color and location information of fire pixels are chosen as sample characteristics, multilayer feed forward neural networks are applied for image segmentation to extract the target of f i re i mages.5. This thesis analyses the current methods of moving detection in fire detecti on, and proposed a moving detection algorithm based on GICA. This algorithm overcomes the shortcomings of the traditional method of the moving detection that bring about noise because of changes i n the adjacent frame gray and inefficient to slow movement.6. This thesis proposes a method of real-time flame detecti on based on visual features of the flame image. First, GICA algorithm is applied to the flame image sequences to extract motion pixels, and then the flame color model establishes and filters the moving pixels movement without the characteristics of flame color pixel. Finally, some classification rules of flame detection establish based on time-frequency domain analysis by wavelet for the f i re al arm.7. This thesis proposes a method of real-time smoke detecti on based on visual features of the smoke image. GICA is applied to the smoke image sequence to extract the moving pixels, and the classificati on rul es establish according to the color of the smoke distribution. Finally, some classification rules of smoke detection establish based on the time-frequency characteristics of the motion pixels by wavelet for the fire alarm.8. At last this thesis summarizes the contents and the innovation of this study, and proposes the further research.