TW201209759A - Image flame detection method - Google Patents

Abstract

An image flame detection method is disclosed. It can acquire moving objects from a digital image acquired by an image acquisition equipment through method of foreground separation and background update. Then color analysis is carried out for filtering moving objects of flameless color system so that moving objects of flame color system is transmitted to the spatial wavelet analysis for filtering moving objects with flameless texture varying characteristics. Then the remaining moving objects are subjected to a spatial band-pass filter for filtering flameless N-dimension texture varying characteristics. Finally, transient noise is filtered through time-domain analysis. Whether a flame is generated is determined by the alarm decision making unit for issuing an alarm.

Description

201209759 DESCRIPTION OF THE INVENTION [Technical Field] The present invention is an image type flame detection method, which is characterized in that image processing is used as an instant flame detection method, and the detected result is displayed on a general display. The instant fire is reported and the alarm is transmitted through the alarm or to the user's receiving device. ° [Prior Art]

In the conventional technology, the traditional flame detection technology cannot be popularized because of the limitation of the detection principle (spatial or distance), and the cost is too high, so that it is impossible to detect early and issue an alarm in the early stage of the flame generation. At present, there are related image-based flame detection studies, but because the image features of the flame are not many, and these features can not effectively distinguish the general image or the flame, the best recognition rate is not high or the problem is too high. It is a flame amplification rate analysis, a difference image analysis, etc., which causes immediate detection and prevention in the early stage of a fire. In the National 2nd Announcement No. 26A patent "Application of image manipulation to detect fire generation methods and systems, = color, brightness and saturation tfl to judge the flame, but this method only uses the preset threshold "Because of the = condition, in the case of different environments, this is a _, it is easy to make a fine-recognition rate and improve the rate. It can be seen that the above-mentioned system still has a design, and it needs to be improved. Tianyou Liangshan New, the case of the various ships derived from the above-mentioned Xishui style, was transferred to the improvement of the creation of the 々 々 经 多年 多年 多年 多年 多年 多年 古 古 古 古 多年 多年 多年 多年 多年 多年 多年 多年 多年 多年 多年 多年 多年 多年 多年 多年 多年Finally, the research and development of silk tree image type flame detection [invention content] The main purpose of the present invention is to use the image analysis shape, and also the butterfly (10) called the wide love 3 201209759 to achieve the purpose of the above invention, the image of the fire detection method, First installed, such as: road, station, power plant, factory, warehouse, machine; and other work platforms, and implement the method on the work platform; #The device can be digital image processing technology on all platforms And use the innovative flame characteristics ^ =, Γ alarm called l to receive the location and location of the flame, and concurrently (four) to receive accurate fire image detection: the method uses the general image to take the device to manipulate the color image, and then through the image features, achieve! ^Transformation, discrete wavelet transform and other techniques' and according to the shadow of the flame = ΓΓ through the foreground separation and background update method to obtain the moving object to the spatial wavelet analysis, and then further 摅 unless the fire pattern = ^ = system of moving objects will be transmitted Brake life 仏 仏 / J ·, - '...' ', 'the moving object of cultural characteristics' of the moving object, the last pass, the non-fire Ν dimension texture change characteristics to determine whether there is a temporary noise generated by The warning decision-making unit is more suitable for the fire-fighting method. When compared with the fine-grained technique, the type, = light-filled heat-sensing mode "has a limit on the two, which effectively improves the detection rate and reduces the false positive rate." Like the sub-method, the traditional method of image wealth or vertical domain image branching is to analyze the image in the radial direction of the object, so it is a good identification of the area; The characteristics of the external expansion, as well as the [Embodiment] Ϊ Ϊ Ϊ Ϊ — — — — — — — — — — — — 为本 为本 为本 为本 四 四 四 四 四 四 四 四 四 流程 流程 流程 流程 流程 流程 流程 流程 流程 流程 流程 流程 流程After obtaining a digital image 9; The digital image of the pickup is subjected to foreground separation 10 and background update u. The foreground separation (1) side 201209759 method finds the difference between the target A input image and the reference image, and if the difference is greater than the critical value, it is the moving area, and then moves. The region can transform the moving region into individual objects through the processing of intrusion, expansion and binary connected images; the background updating method uses a mixed Gaussian model to simulate complex scenes, for each time at different times. A little pixel has established its mixed Gaussian model and updated with time, so the longer the update time, the background model will tend to be stable; 3. The image obtained in step 2 will be color analyzed 12, in terms of rgb color space The flame color is mostly red (R Channei) is larger than the green color (G channel), and the green color is larger than the blue color (B channel), so the flame is very saturated in the image, and the red color is very saturated. These characteristics can be RGB and YCbCr colors. Space to regulate the area of the flame color, defined as follows:

PfgR > PbgR (i)

Pfgcr > Pbgcb (2) In the above formula (1), Pfg and pbg represent the magnitude of the foreground and background, respectively, and R is the R component representing the RGB color space, and in the above formula (2), '& and α are Represents the Cb and Cr components of the yQ£r color space; 4. Separates the high-frequency components in the image by spatial wavelet transform analysis. This high-frequency component can be regarded as the texture of the image. Please refer to the second diagram of the single-layer filter set of the two-dimensional wavelet transform of the present invention. The single-layer filter used by the two-dimensional wavelet transform and the wavelet-converted image of the coefficient will generate a coma image, respectively LL, LH, melon and hh, where lh represents the horizontal 冋 frequency component (horizontal contour strength), and HL represents the vertical high frequency component (the vertical contour detail book represents the high frequency component of the diagonal (pair The rim of the horn is strong and weak.) In order to calculate the high-frequency component cranes of all texture changes, we calculate the high-frequency knives in the region - for the fire-like and animal bodies, the outline of which has a small part in space. The equation for evaluating this change is as follows (4) 5 201209759 ωπ(ηι,η) = |LHn(m,n)p + !HLn(mn)t2 + |HHn(m,n)|2 (3) ωΓ Sizeroov heart 1 emov °Vr (k, 1) (4) ωη'Ιί in the above expression represents two-dimensional wavelet transform of the red component in the image, m〇v is the range of the moving object, and size·^ is the range Size; 5. Use the spatial frequency band filtering method 14 to further analyze the texture of the flame image, Passing the fascinating _ road to the regional information fiber. Please refer to Figure 3 for the spatial filtering of the spatial filtering of the present invention. The recording is from the fixed angle of the moving object area to the boundary path. At this angle, the moving object is divided into N by the object towel, so each object has N-dimensional information, and the information includes changes in the path of each object at different angles. Divide the information of each angle-dimensional wavelet transform to calculate the frequency and degree of its change. The m-group of wavelet transforms is not intended to be shown in Figure 4, and the formula (5) calculates the complexity of each path. Degree P, when p is smaller, 'represents that the change in its path is more gradual, and vice versa, indicating that its change is fierce. Because each image is analyzed in (four) degrees, there are a total of n of these complex data. Neural network (Back (5) Qingxin this (four) n cafe tree to do classifiers] This type of neural network has N inputs, M intermediate nodes, 2 outputs, the architecture diagram shown in Figure 5, where N inputs The value is N different ❼ The degree, the value of ^ the wheel and after the identification of fire or similar to the degree of fire;?! 6 minutes to tense (iv) to reduce the error detection rate, which is based party through the moving average method

6 201209759 The formula 'statistically calculates the temporal analysis rate TAR (Te is the 0ral Analyse Ratio) by the number of facets analyzed by color and spatiality every second, as shown in the following formula (7): (7) Τ.4Ώ - Sin» Of CA Sawplmg Rate and by setting the threshold of TAR where CA represents the cumulative number of warnings (Cumulated Α_), the value can effectively filter out the possibility of false alarms;

7. For the design of the flame design, the index of the refurbishment is made. When the object is known as the flame, the value of the lion can increase, and if the value is less than the threshold, the moving object is Fire and give a warning. The steps included in the present invention 'Firstly, the user can obtain the digital image 9' through the foreground image and the background update u through the image manipulation device, and then perform the color analysis 12 'filtering the movement of the color system. Objects, in order to transmit the moving objects of the fire color to the spatial wavelet analysis 13, and then filter the crane objects unless the texture of the fire changes, and then spatially filter the remaining moving objects. The moving object of the n-dimensional change characteristic is finally passed through the analysis of the Japanese tin, and the filter is temporarily generated. The warning decision unit determines whether there is a flame generated to issue an alarm. Local Moon ~ Image Fire: ^ Detection method, which allows users of the traditional flame detection method to go to the image flame through the method _ situation, the space limit is even in the case of small fire It is possible to issue alarms and minimize the chances of a decision by using the method and test of the Linhupu. It provides users with a robust and stable fire detection technology. [Simplified description of the drawings] Please refer to the detailed system of the present invention and its related points, and the contents of the technology and the purpose of the present invention can be further understood. The related drawings are as follows: Figure 1 is an image flame of the present invention. FIG. 2 is a schematic diagram of a single-layer filter group for two-dimensional wavelet transform according to the present invention; FIG. 3 is a schematic diagram of regional information acquisition for spatial frequency band filtering according to the present invention; Schematic diagram of the converted filter set; Figure 5 is a diagram of the inverse transfer type neural network architecture of the present invention. [Key component symbol description] 9 Digital image 10 Foreground separation 11 Background update 12 Color analysis 13 Spatial wavelet analysis 14 Space band filtering 15 Temporal analysis 16 Warning decision

Claims (1)

201209759 VII. Patent Application Range·· L—Image type flame detection method, the steps include: (a) obtaining digital image through image capturing device; (9) obtaining moving in digital image through foreground separation method and background updating method (<〇 color analysis of moving objects to filter moving objects in the color of fire; (9) spatial wavelet analysis of moving objects of fire color, filtering the texture change characteristics of fire; φ (e) The moving object filtered according to the change characteristic of the fire texture is spatially band-filtered, and the N-dimensional texture change characteristic of the fire is filtered; (1) and through the temporal analysis, the temporarily generated noise is filtered out, and the warning decision unit determines whether there is a flame generated at present. To issue an alarm; (g) If the difference is greater than the critical value, it is the moving area, and then the moving area is transmitted to the individual object through the process of erosion, % expansion and one-value connected image. (8) Update the time zone. Background model; (i) Calculate the high-frequency component strength of all texture changes after two-dimensional wavelet transform; 1 Calculate and evaluate the flame The degree to which the contour of the moving object changes in space; % (k) divides the moving object into N one-dimensional information by the center of the object to include the change in the path of each object at different angles; (l) The information of each angle Calculate the frequency and extent of the change using one-dimensional wavelet transform; (m) Calculate the complexity of each path; (n) Use the Back Propagation Weural Network for the complexity of 1'1 path. To classify to identify a degree similar to fire or non-fire. The image-based flame detection method of claim 1, wherein the step of the foreground separation method may include: finding the current input image and The difference of the reference image. The image flame detection method according to claim 1, wherein the background of the 201209759::: step may include: establishing a high blend for each pixel at different times. 4. 5. 6. 7. The image flame method according to Item 1, wherein the space small two St comprises: two-dimensional wavelet transforming the moving object of the fire color system. Image flame The measurement method, wherein the color framing RGB multi/(10) color space is used to specify the area of the flame color, and the R component between the ycL of the front and the bottom is greater than the R component of the coffee color space of the (4) color; ^ component. The Ο Ο component is larger than the background YCbC> color space with the image type flame method described in item 1 of m2, the space frequency of the towel == contains: recording from the center of the moving object area along the fixed angle to the edge as claimed The image flame method according to the first aspect of the present invention, wherein the temporal analysis system calculates the temporal analysis rate by using a moving average method, and the Nth counts the number of frames of the color and spatial analysis by N per second. By setting the critical value of the temporal analysis rate, the possibility of false alarms is effectively eliminated. ^Apply to the image-based flame side method described in Item 1, the warning decision system increases the value of the energy index when the movement is recognized as a flame, and decreases the value of the energy accumulation index. When it is greater than the critical value, it means that the moving object is a flame and gives a warning. 8.