JP2007272532A - Fire detection apparatus - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a fire detection apparatus capable of extracting a right fire candidate area even if a background is bright, while having a small number of fire candidate areas extracted, so that the amount of computations required to determine whether or not a subject is a fire is small. <P>SOLUTION: The fire detection apparatus includes an image pickup means and an image processing means for detecting a fire from an image picked up. The image processing means has a fire candidate area extracting means for extracting a fire candidate area from the image picked up, and a fire detecting means for detecting whether the fire candidate area extracted is the true fire area. The fire candidate area extracting means creates a difference image between the newest image and the previous image; compares the newest image to the previous image at each pixel to create a high-brightness image whose brightness is the higher of the brightness of the images; multiplies the brightness of the difference image by that of the high-brightness image to create a difference brightness product image; and extracts pixels where the difference brightness product of the difference brightness product image is equal to or higher than a threshold as a fire candidate area. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

The present invention relates to a fire detection apparatus using image processing for fire detection, and more particularly to a fire detection apparatus suitable for use when a light source other than a flame to be monitored is mixed in a monitoring area.

  The conventional fire detection device includes an imaging means, a fire candidate area extracting means for extracting an area having a luminance of a predetermined value or more from the image captured by the imaging means as a fire candidate area, pixel information of the fire candidate area and the fire A fire discriminating means for judging whether or not the object is a flame based on information on the candidate area, and the fire discriminating means includes, as information, the circularity of the object image area and the size of the object image area. The auto-correlation for the temporal dispersion rate and the time change of the size of the object image area is calculated, the circularity of the determined object image area, the temporal dispersion rate of the size of the object image area, the object image It is determined whether or not the object is a flame based on the autocorrelation with respect to the temporal change in the size of the region (see, for example, Patent Document 1).

JP-A-8-305980

However, when detecting a fire in a tunnel, if a candidate fire area is extracted based on the level of brightness, an area having a brightness higher than a predetermined value, such as a rotating light, a vehicle tail lamp, a sodium light, etc. Since all the areas are extracted, the amount of calculation for obtaining the feature quantity of the fire candidate area by the fire discrimination means becomes enormous due to the large number of fire candidate areas, and it takes a long time for the fire discrimination process. There is a problem.
In addition, when a fire candidate area is extracted based only on luminance values, there is a problem that if the background is bright, a correct area cannot be extracted and an area including the background is extracted.

  An object of the present invention is to provide a fire detection device that can extract correct fire candidate areas even when the background is bright, has a small number of extracted fire candidate areas, and requires a small amount of calculation for determining whether the object is a fire. It is to be.

  A fire detection apparatus according to the present invention includes an imaging unit that captures an image of a monitoring area, and an image processing unit that detects a fire in the monitoring area by processing the captured image. A fire detection apparatus comprising: a fire candidate area extracting means for extracting a fire candidate area from a captured image; and a fire detection means for detecting whether the extracted fire candidate area is a real fire area. Is a frame difference processing unit that creates a difference image between the latest image and the immediately preceding image that are time-sequentially continuous, and a high luminance that compares the latest image and the immediately preceding image for each pixel and uses the higher luminance as the luminance. A difference between the high-intensity image generation unit that generates an image, a high-intensity image image generation unit that generates a difference-intensity product image by multiplying the difference image and the luminance of the high-intensity image, and the difference between the difference-intensity product images It has a fire candidate region extraction unit that brightness product is extracted more pixel threshold as fire candidate region.

  The effect of the fire detection apparatus according to the present invention is to multiply a difference image by a high luminance image to obtain a difference luminance product, and when the difference luminance product is equal to or greater than a threshold, the pixel is extracted as a fire candidate region. For this reason, only a bright and moving part can be extracted as a fire candidate area, and a bright but non-moving sodium lamp or a person who moves but is not bright is not extracted as a fire candidate area. Therefore, the number of fire candidate areas can be reduced and the amount of calculation of the feature quantity of the fire candidate areas can be reduced as compared with the extraction based only on the luminance value. Further, since the fire candidate area is extracted in consideration of the difference image, only the bright and moving area like a flame can be correctly extracted as the fire candidate area even if the background is bright.

FIG. 1 is a block diagram of a fire detection apparatus according to the present invention. FIG. 2 is an image taken by a CCD camera that monitors the inside of the tunnel according to the present invention.
As shown in FIG. 1, a fire detection apparatus 1 according to Embodiment 1 of the present invention includes, for example, an image pickup unit 3 as an image pickup unit using a CCD camera 2, and image processing that processes an image taken by the image pickup unit 3. An image processing device 4 as a means and a display unit 5 for displaying information from the image processing device 4 are provided. In addition to the CCD camera 2 used for the image pickup means, any one such as a CMOS camera, an infrared camera or the like that converts an optical signal incident from a monitoring area into an electrical signal and two-dimensionally arranges pixels may be used.

For example, the CCD camera 2 images the monitoring area 30 times per second and outputs 30 images that are continuous in time series.
The CCD camera 2 is installed, for example, at a position overlooking the entire tunnel as a monitoring area. FIG. 2 shows an image taken by the CCD camera 2. As can be seen from this image, the CCD camera 2 is installed, for example, on the upper part of the side wall in the tunnel so as to reflect the direction in which the vehicle 6 runs away. In the image photographed by the CCD camera 2, the rotating lamp 7, the sodium lamp 8, the flame 9 and the person 10 of the vehicle 6 are photographed.

The image processing apparatus 4 includes an image input unit 11 that processes an electric signal photographed and input by the CCD camera 2, a plurality of image memories 12 that store luminance images from the image input unit 11, and a setting value storage that stores setting values. An image processing unit 16 that performs processing based on information from the memory 13, the image memory 12, and the set value storage memory 13 and performs fire discrimination, and an image output unit 17 that outputs image information from the image processing unit 16 to the display unit 5. Have.
The image processing apparatus 4 includes a computer having a CPU, a ROM, a RAM, and an interface circuit.

  The plurality of image memories 12 are ordered so that a predetermined number of images are stored from the latest time-series consecutive images in order from the image memory 12 with a small number of orders. Assigned. When the latest image is stored, the image stored in the image memory 12 with the largest number is deleted, and the image stored in the image memory 12 is stored in the image memory 12 in which it is stored. The image memory 12 is transferred to the image memory 12 having a larger order. Then, the latest image is stored in the image memory 12 with the smallest number.

The image input unit 11 is an analog / digital conversion unit (A / D conversion unit) 21 that converts an electrical signal sent from the CCD camera 2 into a digital signal. The magnitude of the digital signal for each pixel is the luminance of the pixel. A luminance image creating unit 22 that creates a luminance image having the pixel luminance as the pixel value and stores the luminance image in the image memory 12 is provided.
The analog-digital conversion unit 21 converts an analog electric signal photographed by the CCD camera 2 into a digital signal having multiple gradations, for example, 255 gradations for each pixel. In the following description, 11 gradations (0 to 11 floors) are used for ease of explanation.
When the latest image is input, the luminance image creation unit 22 calculates the luminance of the pixel by averaging the digital signal of the pixel for each pixel of the latest image, and creates the luminance image using the luminance of the pixel as the pixel value. To do. In addition, the luminance image creation unit 22 deletes the luminance image stored first, and transfers the luminance image stored in the other image memory 12 to the image memory 12 in which the one old image is stored. Then, the latest luminance image is stored in the image memory 12 assigned so that the newest image is stored.

The image processing unit 16 extracts a fire candidate area extracting unit 25 that extracts a fire candidate area from the luminance image stored in the image memory 12, and whether the fire candidate area extracted by the fire candidate area extracting unit 25 is a true fire area. Fire detection means 26 for determining whether or not to detect a fire is provided.
The fire candidate area extraction unit 25 includes a frame difference processing unit 31 that obtains a difference between the brightness of the latest brightness image and the brightness of the immediately preceding brightness image for each pixel and creates a difference image.

Further, the fire candidate area extraction unit 25 compares the luminance of the latest luminance image with the luminance of the immediately preceding luminance image for each pixel, and creates a high luminance image having the higher luminance as the pixel value. The image creation unit 32 includes a difference brightness product image creation unit 33 that creates a difference brightness product image having a difference brightness product obtained by multiplying the difference image and the brightness of the high brightness image as a pixel value for each pixel.
In addition, the fire candidate area extraction unit 25 determines, for each pixel, whether or not the difference luminance product of the difference luminance product image is equal to or greater than a predetermined threshold stored in the setting value storage memory 13, and the difference luminance product is equal to or greater than the threshold. At this time, this pixel is extracted as a fire candidate pixel, and when another fire candidate pixel is adjacent to the extracted fire candidate pixel, it is connected to have a fire candidate region extraction unit 34 as a fire candidate region.

The fire detection unit 26 includes a fire candidate area feature amount calculation unit 36, a frequency analysis calculation unit 37, and a fire determination unit 38.
The fire candidate area feature quantity calculating means 36 calculates a feature quantity of the fire candidate area, for example, average luminance or area over a predetermined time.
The frequency analysis calculation unit 37 includes a wavelet transform calculation unit and a two-dimensional FFT unit. The frequency analysis calculation unit 37 performs timelet data on the time-series data of the feature amount calculated by the fire candidate region feature amount calculation unit 36 to obtain a time-frequency spectrum. Ask for images. Then, the two-dimensional FFT means performs a two-dimensional FFT on the image of the time-frequency spectrum to obtain an image indicating the time axis distribution and the frequency axis distribution. In addition, the minimum value of the frequency centroid position and the ratio of variance, and the ratio of the centroid average position of the X axis and the centroid average position of the Y axis are calculated from the created images.
The fire discriminating means 38 is extracted by comparing with a predetermined value on the basis of the ratio of the calculated minimum value of the center of gravity of the frequency and the variance, and the ratio of the center of gravity average position of the X axis and the center of gravity center of the Y axis It is determined whether or not the fire candidate area is a fire (flame).

  FIG. 3 is a flowchart showing a procedure for extracting a fire candidate area. FIG. 4 is a luminance image that is used or created in the procedure for extracting the fire candidate area and that displays the luminance as a numerical value. In FIG. 4, the case of the flame 9, the rotating lamp 7, the sodium lamp 8, and the person 10 is illustrated separately for explanation. FIG. 4 is a diagram for explaining how each region in the captured image of FIG. 2 is processed by the processing of the image processing unit 16. FIG. 4A shows a luminance image taken immediately before the latest image taken by the CCD camera 2 most recently. FIG. 4B is a luminance image taken at the latest time point, and typically shows an image of 3 pixels vertically and horizontally (pixels) with 11 gradations.

Here, characteristics of each region (flame, sodium lamp, person) in the immediately preceding image (a) and the latest image (b) in FIG. 4 will be described.
(1) In the case of a flame and a rotating lamp This area has a high luminance value and moves. However, in the case of a flame, since the moving part is limited to the upper part, there is almost no change in the luminance value of the lower region between the immediately preceding image (a) and the latest image (b).
(2) Sodium lamp (in case of fixed light source)
This area has a high luminance value, but has almost no movement (change) in the immediately preceding image (a) and the latest image (b). Even if the luminance value changes for some reason (for example, the upper left cell, the lower right cell), there is no large change like the flame region, so in the difference image (c), the value of the difference value is small. The luminance product does not exceed a predetermined value.
(3) In the case of a person This area has a low luminance value because it moves but does not emit light originally. For this reason, a difference value is generated in the difference image (c), but since the luminance value is low, even if the difference luminance product (e) is obtained, the predetermined value is not exceeded.

Next, a procedure for extracting a fire candidate area will be described with reference to FIG.
The luminance images shown in FIGS. 4A and 4B are stored in the image memory 12 as the immediately preceding image and the latest image, respectively.
In step S101, the frame difference processing unit 31 reads the latest image (luminance image shown in FIG. 4B) and the immediately preceding image (luminance image shown in FIG. 4A) from the image memory 12, and corresponding pixels. The difference is calculated for each time, the absolute value thereof is calculated, and a difference image in which the difference absolute value shown in FIG.
In step S102, the high brightness image creation unit 32 compares the brightness of the latest image (FIG. 4B) and the immediately preceding image (FIG. 4A) for each corresponding pixel. A high-intensity image (FIG. 4 (d)) in which is the pixel value is created.
In step S103, the difference luminance product image creation unit 33 multiplies the difference absolute value of the difference image shown in FIG. 4C by the luminance of the high luminance image shown in FIG. A difference luminance product image having the difference luminance product shown in (e) as a pixel value is created.
In step S104, the fire candidate area extraction unit 34 determines whether or not the difference luminance product of the difference luminance product image is 16 or more set as a predetermined threshold value. If the difference luminance product is 16 or more, the pixel is fired. A fire candidate pixel image is created as a candidate pixel.
Prior to the comparison with the threshold value, the difference luminance product image (FIG. 4 (e)), for example, averages the luminance product values after accumulating the images over about 4 seconds (total of 120 images). Like that. At this time, the difference luminance product image may be subjected to LPF processing in the time axis direction. In this way, an image in which a bright and moving part is emphasized can be created.
In step S105, it is determined whether or not there are other fire candidate pixels adjacent to the fire candidate pixel. When there are adjacent fire candidate pixels, the pixels are connected to each other, and when there is no adjacent fire candidate pixel, only Are extracted as fire candidate areas.

In this way, by creating a difference image of the luminance between the latest image and the immediately preceding image, for example, the sodium lamp 8 that has almost no change in luminance even if the luminance value is high can be excluded from the fire candidate region, and the difference luminance product is Depending on whether or not it exceeds the predetermined threshold, it is possible to remove from the fire candidate area a thing with a relatively small brightness as a human being, but the area remaining as a fire candidate area is a bright and moving real flame 9 and the revolving light 7 can be reduced, and the amount of calculation for obtaining the feature quantity of the fire candidate area is reduced in the fire detection means 26.
Further, since the fire candidate area is extracted in consideration of the difference image, only the bright and moving area like a flame can be correctly extracted as the fire candidate area even if the background is bright.

The true flame 9 also has a high luminance at the base of the flame 9 but a small change in luminance, so that it can be narrowed down to a smaller area as a fire candidate area. The amount of calculation for obtaining the quantity is further reduced.
In addition, when comparing the pixel value of the difference luminance product and the predetermined value, as described in the embodiment, it may be performed for each pixel, but a plurality of pixels are collected as one block, and a threshold value is set for each block. A comparison may be made. For example, when the screen is divided into about 1200 in a grid pattern and the sum of the difference luminance products in each block exceeds a threshold value, the block area may be set as a fire candidate area (also referred to as an attention area). Good.

Here, the threshold value will be described. When the luminance value is represented by 255 gradations, when the luminance value of the immediately preceding image is 0 and the luminance value of the latest image is 255, the difference value is 255 and the luminance value of the high luminance image is 255. Therefore, the value of the difference luminance product image is 255 × 255, which is 65025. This is the maximum value that the difference luminance product image can take.
In the case of a fire, for example, the latest image has a value of about 200 and the immediately preceding image has a value of about 180. Therefore, since the difference value is 20 and the value of the high-intensity image is 200, the difference luminance product image is 4000. For this reason, when the luminance value is 255 gradations, about 4000 can be used as a threshold standard.

Next, the operation of the fire detection means 26 will be described. First, the fire candidate area feature amount calculation unit 36 calculates the feature amount of the fire candidate area extracted by the fire candidate area extraction unit 25 over a predetermined time. Here, for example, the average brightness of the fire candidate area is calculated over a predetermined time, and data as shown in FIG. 5 is collected.
The wavelet transform computing means constituting the frequency analysis computing means 37 performs wavelet transformation on the time series data of the average luminance, and the time-frequency spectrum is shown with time on the horizontal axis and frequency on the vertical axis shown in FIG. Get an image. Here, the time-frequency spectrum shown in FIG. 6A is for the case where the fire candidate region is the flame 9, and the time-frequency spectrum shown in FIG. 6B is for the case where the fire candidate region is the rotating lamp 7. .
For reference, FIG. 8 shows an original image of the time-frequency spectrum shown in FIG.

  As can be seen from the image of the time-frequency spectrum of FIG. 6, in the case of the rotating lamp 7, the frequency is constant over time, whereas in the case of the flame 9, the frequency is constant over time. It is constantly changing. If this is expressed by some evaluation value, it is possible to identify whether the candidate fire area is due to the flame 9 or the rotating lamp 7. There are various methods for obtaining the evaluation value. Here, the minimum value of the frequency centroid position and the variance ratio are calculated in the image of FIG.

In the image of the time-frequency spectrum of FIG. 6, in the case of a rotating lamp, the value of a certain high frequency is taken compared to the case of a flame, and the value does not change over time and is almost constant. Takes a value. That is, if the dispersion value calculation means (not shown) calculates the dispersion value V from the image of the time-frequency spectrum, the dispersion value V becomes a large value in the case of a flame, and a small value in the case of a rotating lamp. Become.
Further, if a center-of-gravity calculating means (not shown) that calculates the position of the center of gravity of the frequency axis (Y-axis) obtains the center of gravity Hy from the image of the time-frequency spectrum and obtains the minimum value MinHy of the center of gravity Hy, The minimum value MinHy of the center of gravity is a small value in the case of a flame, and is a large value in the case of a rotating lamp.
Therefore, if the evaluation value S = minimum value MinHy / dispersion V of the center of gravity, in the case of a flame, the evaluation value S is very small compared to the rotating lamp. Can be identified. The variance value calculating means and the centroid calculating means described here are an example of an evaluation value calculating means for calculating the evaluation value by obtaining the variance and the centroid.

Further, a two-dimensional FFT unit, which is another unit constituting the frequency analysis calculation unit 37, will be described. The two-dimensional FFT means performs a two-dimensional FFT process on the time-frequency spectrum of FIG. 6, and the horizontal axis indicates the time axis distribution and the vertical axis indicates the frequency axis distribution (complexity) shown in FIG. Ask for an image. Here, the case shown in FIG. 7A is a case of flame, and the case shown in FIG. 7B is a case of a rotating lamp. In FIG. 7, the white part is the place where the intensity is the highest.
In FIG. 7, in the case of the flame 9, it can be seen that the time axis is distributed up to a high frequency and the frequency axis is distributed at a low frequency. On the other hand, in the case of the rotating lamp 7, it can be seen that the time axis is distributed at a low frequency and the frequency axis is distributed to a high frequency. Here, from the image of FIG. 7, the ratio between the X-axis center of gravity average position and the Y-axis center of gravity average position is calculated to obtain the evaluation value.

  The fire discriminating means 38 in FIG. 6 and FIG. 7 is based on the ratio of the minimum frequency MinHy and the variance V of the calculated frequency center of gravity position, and the ratio of the X axis center of gravity center position to the Y axis center of gravity center position. It is compared with a predetermined value to determine whether or not there is a fire (flame).

It is a block diagram of the fire detection apparatus concerning this invention. It is the image which the CCD camera which monitors the inside of the tunnel concerning this invention image | photographs. It is a flowchart which shows the procedure which extracts a fire candidate area | region. This is a luminance image that is used or created in the procedure for extracting a fire candidate area and that displays the luminance as a numerical value. It is the graph which plotted the change of the average brightness | luminance of a fire candidate area | region. It is the time-frequency spectrum obtained by wavelet transforming the change of the average brightness of the fire candidate area. 7 is a two-dimensional FFT result obtained by two-dimensional FFT of the time-frequency spectrum of FIG. It is the original image of the time-frequency spectrum shown in FIG.

Explanation of symbols

  1 Fire detection device, 2 CCD camera, 3 imaging unit, 4 image processing device, 5 display unit, 6 vehicle, 7 rotating lamp, 8 sodium lamp, 9 flame, 10 human, 11 image input unit, 12 image memory, 13 setting Value storage memory, 16 image processing unit, 17 image output unit, 21 analog-digital conversion unit (A / D conversion unit), 22 luminance image creation unit, 25 fire candidate area extraction unit, 26 fire detection unit, 31 frame difference processing unit 32 high luminance image creation unit, 33 differential luminance product image creation unit, 34 fire candidate area extraction unit, 36 fire candidate area feature amount calculation means, 37 frequency analysis calculation means, 38 fire discrimination means.

Claims (2)

The image processing unit includes an image capturing unit that captures an image of the monitoring area and an image processing unit that detects a fire in the monitoring area by processing the captured image, and the image processing unit extracts a fire candidate area from the captured image. In a fire detection apparatus having a fire candidate area extraction means and a fire detection means for detecting whether the extracted fire candidate area is a true fire area,
The fire candidate area extraction means
A frame difference processing unit that creates a difference image between the latest image and the immediately preceding image that are continuous in time series;
A high-brightness image creating unit that creates a high-brightness image in which the brightness of the higher brightness is compared by comparing the latest image and the immediately preceding image for each pixel;
A difference luminance product image creating unit for creating a difference luminance product image by multiplying the difference image and the luminance of the high luminance image;
A candidate fire area extraction unit that extracts, as a fire candidate area, a pixel whose difference brightness product of the difference brightness product image is equal to or greater than a threshold;
A fire detection device characterized by comprising: The fire detection means is
A fire candidate area feature quantity computing means for computing the feature quantity of the fire candidate area over a predetermined time, and time-series data of the feature quantity computed by the fire candidate area feature quantity computing means by performing wavelet transform to obtain a time The wavelet transform calculating means for obtaining an image of a frequency spectrum, and an evaluation value calculating means for calculating an evaluation value by obtaining a centroid and a variance from the image of the time-frequency spectrum. Fire detection device.