CN112580396A - Forest fire recognition method - Google Patents
Forest fire recognition method Download PDFInfo
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- CN112580396A CN112580396A CN201910931755.6A CN201910931755A CN112580396A CN 112580396 A CN112580396 A CN 112580396A CN 201910931755 A CN201910931755 A CN 201910931755A CN 112580396 A CN112580396 A CN 112580396A
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- 238000000034 method Methods 0.000 title claims abstract description 22
- 239000000779 smoke Substances 0.000 claims abstract description 21
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 20
- 239000003595 mist Substances 0.000 claims abstract description 18
- 238000012549 training Methods 0.000 claims abstract description 14
- 238000012544 monitoring process Methods 0.000 claims abstract description 11
- 238000012360 testing method Methods 0.000 claims abstract description 7
- 238000012545 processing Methods 0.000 claims description 11
- 238000007781 pre-processing Methods 0.000 claims description 4
- 238000001914 filtration Methods 0.000 claims description 3
- 230000000877 morphologic effect Effects 0.000 claims description 3
- 230000002265 prevention Effects 0.000 abstract description 4
- 238000012935 Averaging Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 238000002372 labelling Methods 0.000 description 2
- 241000282414 Homo sapiens Species 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005728 strengthening Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
- G06V10/462—Salient features, e.g. scale invariant feature transforms [SIFT]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
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- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Computational Linguistics (AREA)
- Software Systems (AREA)
- Fire-Detection Mechanisms (AREA)
Abstract
一种森林火灾识别方法，涉及图像识别技术领域，为解决现有技术中不能及时的发现森林火灾的问题，包括步骤一：获取监控区域内的视频图像；步骤二：根据图像信息对烟雾图像或水雾图像的类型进行判断，然后对图像中树木所在区域进行定位；步骤三：对获取到的图像进行预处理；步骤四：将处理后的图像进行感兴趣区域提取；步骤五：将训练集中烟雾或水雾图像信息按上述步骤得到切割后的图像信息，并利用SVM分类器进行训练；步骤六：输入测试集，得到识别结果。本发明可以全面的监控森林防火区域，及时的发现监控区域内的火灾情况，并及时报警。A forest fire identification method relates to the technical field of image identification. In order to solve the problem that forest fires cannot be detected in time in the prior art, the method includes step 1: acquiring a video image in a monitoring area; step 2: analyzing smoke images or smoke images according to image information Determine the type of water fog image, and then locate the area where the tree is located in the image; Step 3: Preprocess the acquired image; Step 4: Extract the region of interest from the processed image; Step 5: Put the training set The image information of smoke or water mist is obtained according to the above steps, and the image information after cutting is obtained, and the SVM classifier is used for training; Step 6: Input the test set to obtain the recognition result. The invention can comprehensively monitor the forest fire prevention area, discover the fire situation in the monitoring area in time, and give an alarm in time.
Description
Technical Field
The invention relates to the technical field of image recognition, in particular to a forest fire recognition method.
Background
Forest fire refers to the action of forest fire which loses artificial control, freely spreads and expands in forest lands and brings certain harm and loss to forests, forest ecosystems and human beings. Forest fires are natural disasters which are strong in burst, large in destructiveness and difficult to dispose and rescue.
The forest fire prevention work is an important component of the Chinese disaster prevention and reduction work, is important content of the construction of the national public emergency system, is an important guarantee of social stability and the people's living and entertainment industry, is an important guarantee for accelerating the development of the forest industry and strengthening the basis and the premise of the ecological construction, and relates to forest resources and ecological safety, and the life and property safety of people, while the timely discovery of forest fires can greatly reduce the property loss and can quickly control the fire behavior.
Disclosure of Invention
The purpose of the invention is: aiming at the problem that the forest fire cannot be found in time in the prior art, the forest fire identification method is provided.
The technical scheme adopted by the invention to solve the technical problems is as follows: a forest fire recognition method comprises the following steps:
the method comprises the following steps: acquiring a video image in a monitoring area;
step two: judging the type of the smoke image or the water mist image according to the image information, and then positioning the area where the tree is located in the image;
step three: preprocessing the acquired image;
step four: extracting the region of interest of the processed image;
step five: obtaining cut image information from the smog or water mist image information in the training set according to the steps, and training by utilizing an SVM classifier;
step six: and inputting the test set to obtain a recognition result.
Further, the detailed steps of the second step are as follows:
step two, firstly: obtaining an image containing smoke or water mist;
step two: carrying out gray level processing on the smoke image or the water mist image;
step two and step three: and judging the type of the image according to whether continuous ripples exist in the image, labeling the image, and taking the image as a training set, wherein the classification result is smoke or water mist.
Further, the detailed steps of the fourth step are as follows:
step four, firstly: multiplying and averaging values in the x direction and the y direction by using a Sobel edge enhancement method, and then carrying out binarization processing;
step four and step two: numbering the profile;
step four and step three: and carrying out linear approximation value taking on the coordinate values of the phase points by a least square method according to the serial number of the profile.
Further, the images in the training set and the test set are in an RGB format.
Further, the preprocessing in the third step comprises: filtering, binarization and morphological processing.
Further, the acquisition of the video image is completed through a monitoring camera installed in the monitored area.
The invention has the beneficial effects that: the invention can comprehensively monitor the forest fire prevention area, timely discover the fire condition in the monitoring area and timely give an alarm, can discover the abnormal state at the first time and send an alarm, reduces the workload of monitoring personnel, improves the working efficiency by more than one time, and realizes the effective monitoring of the hidden danger of forest fire within 24 hours.
Detailed Description
The first embodiment is as follows: the forest fire recognition method in the embodiment comprises the following steps:
the method comprises the following steps: acquiring a video image in a monitoring area;
step two: judging the type of the smoke image or the water mist image according to the image information, and then positioning the area where the tree is located in the image;
step three: preprocessing the acquired image;
step four: extracting the region of interest of the processed image;
step five: obtaining cut image information from the smog or water mist image information in the training set according to the steps, and training by utilizing an SVM classifier;
step six: and inputting the test set to obtain a recognition result.
The second embodiment is as follows: this embodiment mode is further described with reference to the first embodiment mode, and the difference between this embodiment mode and the first embodiment mode is that the detailed steps of the second step mode are:
step two, firstly: obtaining an image containing smoke or water mist;
step two: carrying out gray level processing on the smoke image or the water mist image;
step two and step three: and judging the type of the image according to whether continuous ripples exist in the image, labeling the image, and taking the image as a training set, wherein the classification result is smoke or water mist.
In the embodiment, when mist is judged to be smoke or water mist, whether continuous ripples exist in a finally obtained image is judged, if the continuous ripples exist, the image is the smoke, and if the continuous ripples do not exist, the image is the water mist.
In the specific implementation of the invention, when the smoke is judged, the alarm processing is carried out, and if the smoke is water mist, the alarm processing is not carried out.
The third concrete implementation mode: this embodiment mode is further described with reference to the first embodiment mode, and the difference between this embodiment mode and the first embodiment mode is that the detailed step of the fourth step is:
step four, firstly: multiplying and averaging values in the x direction and the y direction by using a Sobel edge enhancement method, and then carrying out binarization processing;
step four and step two: numbering the profile;
step four and step three: and carrying out linear approximation value taking on the coordinate values of the phase points by a least square method according to the serial number of the profile.
In the embodiment, a line appears in the image after the image is processed, one end of the line is a fire point, and the smoke image is formed around the fire point.
The fourth concrete implementation mode: the present embodiment is further described with respect to the first embodiment, and the difference between the present embodiment and the first embodiment is that the training set and the test set all use RGB formats.
The fifth concrete implementation mode: the present embodiment is further described with reference to the first embodiment, and the difference between the present embodiment and the first embodiment is that the pretreatment in the third step includes: filtering, binarization and morphological processing.
The sixth specific implementation mode: the present embodiment is described in further detail with reference to the first embodiment, and the difference between the present embodiment and the first embodiment is that the video image is acquired by a monitoring camera installed in a monitored area.
It should be noted that the detailed description is only for explaining and explaining the technical solution of the present invention, and the scope of protection of the claims is not limited thereby. It is intended that all such modifications and variations be included within the scope of the invention as defined in the following claims and the description.
Claims (6)
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| CN201910931755.6A CN112580396A (en) | 2019-09-29 | 2019-09-29 | Forest fire recognition method |
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| CN201910931755.6A CN112580396A (en) | 2019-09-29 | 2019-09-29 | Forest fire recognition method |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114863350A (en) * | 2022-07-06 | 2022-08-05 | 江苏开放大学（江苏城市职业学院） | Forest monitoring method and system based on image recognition |
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