CN112215173A - Forest fire monitoring system based on density peak value adaptive clustering - Google Patents
Forest fire monitoring system based on density peak value adaptive clustering Download PDFInfo
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- G06V20/10—Terrestrial scenes
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B17/00—Fire alarms; Alarms responsive to explosion
- G08B17/005—Fire alarms; Alarms responsive to explosion for forest fires, e.g. detecting fires spread over a large or outdoors area
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- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
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- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B17/00—Fire alarms; Alarms responsive to explosion
- G08B17/12—Actuation by presence of radiation or particles, e.g. of infrared radiation or of ions
- G08B17/125—Actuation by presence of radiation or particles, e.g. of infrared radiation or of ions by using a video camera to detect fire or smoke
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Abstract
The invention discloses a forest fire monitoring system based on density peak value adaptive clustering, which comprises: shooting an unmanned aerial vehicle, wherein an infrared temperature detector and a temperature threshold value are arranged in the unmanned aerial vehicle; the unmanned aerial vehicle control system is in communication connection with the shooting unmanned aerial vehicle; the data processing system is coupled with the shooting unmanned aerial vehicle and used for receiving the shooting image output by the shooting unmanned aerial vehicle; and the fire alarm prediction system is coupled with the data processing system to receive the clustering result output by the data processing system and analyze and calculate the clustering result to output a fire alarm prediction result. According to the forest fire monitoring system based on the density peak value adaptive clustering, whether the current forest has a fire or not can be effectively analyzed by using a density peak value clustering algorithm through the arrangement of the data processing system.
Description
Technical Field
The invention relates to a fire monitoring system, in particular to a forest fire monitoring system based on density peak value adaptive clustering.
Background
The key point of forest fire prevention is to monitor the early warning to forest fire, and at present, forest fire prevention divide into artifical inspection, include: monitoring by adopting a smoke and fire sensor and a camera at fixed positions; various civil aircrafts are used for inspecting fire points; the manual mountain patrol inspection is adopted, the cost is low, the forest fire prevention mode is more in use at present, and the efficiency is low. Because the forest area is large, the width of the forest workers is wide, the distance from the city is far, and the ability of identifying through human eyes is limited, the early-stage fire which does not form a flame can not be identified, and the early-stage fire can not be identified quickly. In order to effectively monitor large-area forests, mountainous regions and other terrains, a smoke and fire sensor at a fixed position needs to be installed, and the smoke and fire sensor has strong flame early warning capability. However, due to unstable factors of terrains such as forests and mountains, installation of the smoke and fire sensor is limited, and it is difficult to select a proper place to install the smoke and fire sensor, so that dead angles can be observed. The firework sensor needs to be supported by a large number of sensors to play a role, and the maintenance cost of the firework sensor is not a small expense, so that the fire prevention cost of a forest farm is greatly increased. In order to effectively and timely find the forest fire, various civil aircrafts are allocated to the places, the civil aircrafts can effectively and timely find the forest fire, the condition of observing dead angles does not exist, the leasing cost of the civil aircrafts is not low, the fireproof cost is increased, and the fire high-risk points of the covering can also influence the effect of patrol inspection.
Therefore, the system and the method have the patent number of 2020103424646, the patent name is a system and a method suitable for forest fire risk monitoring, forest images are shot by arranging an unmanned aerial vehicle control system and an unmanned aerial vehicle, then the forest images shot by the unmanned aerial vehicle are processed by a data processing and communication system, a target picture is detected and trained by utilizing a deep learning image processing algorithm, an unmanned aerial vehicle forest fire risk monitoring model is built, the current fire situation is reflected by the detection model, however, the forest situation is complex, whether the current forest is in fire or not is judged by a color recognition mode in a comparison file 1, on one hand, the calculated amount is very large, the equipment cost is increased, the calculation time is also increased, on the other hand, the collected images have too many interference elements, and the calculation precision is greatly reduced.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a forest fire monitoring system based on density peak value adaptive clustering, which is less in calculated amount and high in calculation precision.
In order to achieve the purpose, the invention provides the following technical scheme: a forest fire monitoring system based on adaptive clustering of density peaks comprises:
the shooting unmanned aerial vehicle is provided with an infrared temperature detector and a temperature threshold value, is used for flying over a forest, shoots and monitors the temperature condition of the forest so as to output a shot image, and marks a place exceeding the temperature threshold value on the shot image;
the unmanned aerial vehicle control system is in communication connection with the shooting unmanned aerial vehicle so as to control the flight track of the shooting unmanned aerial vehicle on the forest;
the data processing system is coupled with the shooting unmanned aerial vehicle and used for receiving the shot images output by the shooting unmanned aerial vehicle and clustering the places in the shot images through a density peak value clustering algorithm to obtain clustering results;
and the fire alarm prediction system is coupled with the data processing system to receive the clustering result output by the data processing system and analyze and calculate the clustering result to output a fire alarm prediction result.
As a further improvement of the present invention, the data processing system clusters the locations in the captured image by using a density peak clustering algorithm, and the specific steps are as follows:
the method comprises the following steps that firstly, the flight path of a shooting unmanned aerial vehicle in an unmanned aerial vehicle control system is uniformly divided into a plurality of sections, and the local density of each place in a shooting image is calculated by taking the distance length of one section as a truncation distance;
and step two, selecting a clustering center place, classifying the rest places into the category to which the closest class center with the density higher than that of the other places belongs, completing a clustering algorithm, and outputting a clustering result.
As a further improvement of the present invention, the truncation distance in the first step is calculated by the following steps:
step one, after a shot image marked with a place is received, integrating a flight track of the shot unmanned aerial vehicle into the shot image, simultaneously extending all points in the shot image onto the flight track, and forming cut-off points on the flight track
And step two, calculating the distance between the truncation points in the step one to obtain a truncation line set, and calculating the median of the length of the truncation line in the truncation line set, wherein the median is used as the truncation distance.
As a further improvement of the present invention, the specific steps of the fire alarm prediction system for analyzing and calculating the clustering result are as follows:
step three, after the clustering result is received, circling each cluster, and calculating the number of places in each cluster of the clustering result;
selecting clusters with a large number of places as fire pre-fire rings, sending a signal to an unmanned aerial vehicle control system, controlling the shooting unmanned aerial vehicle to move to the position of the fire pre-fire rings by the unmanned aerial vehicle control system, and shooting specific images of the fire pre-fire rings by descending the height;
and step five, analyzing whether the specific image obtained in the step four has a flame image or not through an image recognition algorithm, outputting a fire early warning signal if the specific image has the flame image, outputting a fire attention signal if the specific image does not have the flame image, and simultaneously dispatching the fire detection robot to move to a fire pre-distribution ring for detection.
As a further improvement of the invention, the specific steps of identifying the flame image by the image identification algorithm in the fifth step are as follows:
fifthly, traversing each color in the specific image and drawing a color boundary;
identifying the red color boundary in the fifth step, amplifying the internal image of the color boundary, and analyzing the pixels of the internal image of the color boundary;
and step three, comparing the image pixel value obtained in the step two with the flame pixel value on the existing network, judging as a flame image if the comparison results are similar, and judging as a non-flame image if the comparison results are different.
The method has the advantages that the method can effectively fly to the upper space of the forest by shooting the unmanned aerial vehicle, the overlook image of the forest is shot, the shot image is only used as a background, the places exceeding the temperature threshold can be marked on the background image through the infrared temperature detector and the temperature threshold in the shot image, and then the places exceeding the temperature threshold can be subjected to cluster analysis through the data processing system and the fire alarm prediction system, so that compared with the method of adopting image analysis in the prior art, the method only needs to perform cluster analysis on the places, the calculation amount is low, the equipment cost is reduced, the calculation time is saved, and the problem of reduction of calculation accuracy caused by the influence of interference elements on the image is solved.
Drawings
FIG. 1 is a block diagram of a forest fire monitoring system based on adaptive clustering of density peaks according to the present invention.
Detailed Description
The invention will be further described in detail with reference to the following examples, which are given in the accompanying drawings.
Referring to fig. 1, the forest fire monitoring system based on density peak adaptive clustering of this embodiment is characterized in that: the method comprises the following steps:
the shooting unmanned aerial vehicle 1 is internally provided with an infrared temperature detector and a temperature threshold value, is used for flying over a forest, shoots and monitors the temperature condition of the forest so as to output a shot image, and marks a place exceeding the temperature threshold value on the shot image;
the unmanned aerial vehicle control system 2 is in communication connection with the shooting unmanned aerial vehicle 1 so as to control the flight track of the shooting unmanned aerial vehicle 1 on the forest;
the data processing system 3 is coupled to the shooting unmanned aerial vehicle 1 to receive the shot images output by the shooting unmanned aerial vehicle 1, and clusters the places in the shot images through a density peak value clustering algorithm to obtain clustering results;
the fire prediction system 4 is coupled with the data processing system 3 to receive the clustering result output by the data processing system 3, and analyze and calculate the clustering result to output the fire prediction result, in the process of using the monitoring system of the embodiment, firstly, the unmanned aerial vehicle control system 2 controls the shooting unmanned aerial vehicle 1 to fly to the upper part of the forest to be detected to shoot the image, and outputs the image marked with the place, and then the image is transmitted into the data processing system 3, and the clustering is carried out through the density peak value clustering algorithm in the data processing system 3, so that the cluster with large place density can be obtained, so that the high temperature places in the area are represented, the condition of fire is likely to occur, because the temperature of one area is increased when the fire occurs, and the point of fire possibly occurring in the shooting image can be simply and effectively analyzed through the clustering function, compared with the image analysis mode in the prior art, the method has the advantages that the calculated amount is lower, and the corresponding calculation precision is higher.
As an improved specific embodiment, the specific steps of the data processing system 3 clustering the positions in the shot image by the density peak clustering algorithm are as follows:
the method comprises the following steps that firstly, the flight path of a shooting unmanned aerial vehicle 1 in an unmanned aerial vehicle control system 2 is uniformly divided into a plurality of sections, and the distance length of one section is used as a truncation distance to calculate the local density of each place in a shooting image;
and step two, selecting a clustering center place, classifying other places into a category to which the most similar class center with the density higher than that of the clustering center place belongs, finishing a clustering algorithm, and outputting a clustering result.
As a specific embodiment of the improvement, the truncation distance in the first step is calculated by the following steps:
step one, after the shot image marked with the place is received, the flight track of the shot unmanned aerial vehicle 1 is integrated into the shot image, meanwhile, all points in the shot image extend to the flight track, and the interception points are formed on the flight track
And step two, calculating the distance between the truncation points in the step one to obtain a truncation line set, and calculating the median of the lengths of the truncation line segments in the truncation line set, wherein the median is used as the truncation distance.
As an improved specific embodiment, the specific steps of the fire alarm prediction system 4 performing analysis and calculation on the clustering result are as follows:
step three, after the clustering result is received, circling each cluster, and calculating the number of places in each cluster of the clustering result;
selecting clusters with a large number of places as fire pre-fire rings, sending a signal to the unmanned aerial vehicle control system 2, controlling the shooting unmanned aerial vehicle 1 to move to the position of the fire pre-fire rings by the unmanned aerial vehicle control system 2, and shooting specific images of the fire pre-fire rings by descending the height;
step five, whether the specific image obtained in the step four has a flame image is analyzed through an image recognition algorithm, if the flame image exists, a fire early warning signal is output, if the flame image does not exist, a fire attention signal is output, and meanwhile, a fire detection robot is dispatched to move into a fire pre-launching loop to detect. As an improved specific implementation manner, the specific steps of the image recognition algorithm in the fifth step of recognizing the flame image are as follows:
fifthly, traversing each color in the specific image and drawing a color boundary;
identifying the red color boundary in the fifth step, amplifying the internal image of the color boundary, and analyzing the pixels of the internal image of the color boundary;
step five, comparing the image pixel value obtained in the step five with the flame pixel value on the existing network, judging the image to be a flame image if the comparison results are similar, and judging the image to be a non-flame image if the comparison results are different.
In conclusion, the monitoring system of the embodiment can shoot the images with the location identifications by shooting the unmanned aerial vehicle 1 through the shooting of the unmanned aerial vehicle 1, the unmanned aerial vehicle control system 2, the data processing system 3 and the fire prediction system 4, and then can effectively monitor the forest fire by using the density peak value clustering algorithm of the data processing system 3.
The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may occur to those skilled in the art without departing from the principle of the invention, and are considered to be within the scope of the invention.
Claims (5)
1. The utility model provides a forest fire monitoring system based on adaptive clustering of density peak value which characterized in that: the method comprises the following steps:
the shooting unmanned aerial vehicle (1) is internally provided with an infrared temperature detector and a temperature threshold value, is used for flying over a forest, shooting and monitoring the temperature condition of the forest so as to output a shot image, and simultaneously marking a place exceeding the temperature threshold value on the shot image;
the unmanned aerial vehicle control system (2) is in communication connection with the shooting unmanned aerial vehicle (1) to control the flight track of the shooting unmanned aerial vehicle (1) on the forest;
the data processing system (3) is coupled with the shooting unmanned aerial vehicle (1) and used for receiving the shot images output by the shooting unmanned aerial vehicle (1) and clustering the places in the shot images through a density peak value clustering algorithm to obtain clustering results;
and the fire alarm prediction system (4) is coupled with the data processing system (3) to receive the clustering result output by the data processing system (3) and analyze and calculate the clustering result to output a fire alarm prediction result.
2. The forest fire monitoring system based on density peak adaptive clustering of claim 1, wherein: the data processing system (3) clusters the places in the shot image through a density peak clustering algorithm, and the specific steps are as follows:
the method comprises the following steps that firstly, the flight path of a shooting unmanned aerial vehicle (1) in an unmanned aerial vehicle control system (2) is uniformly divided into a plurality of sections, and the distance length of one section is used as a truncation distance to calculate the local density of each place in a shooting image;
and step two, selecting a clustering center place, classifying the rest places into the category to which the closest class center with the density higher than that of the other places belongs, completing a clustering algorithm, and outputting a clustering result.
3. The forest fire monitoring system based on density peak adaptive clustering of claim 2, wherein: the truncation distance in the first step is calculated by the following steps:
step one, after a shot image marked with a place is received, integrating the flight track of the shot unmanned aerial vehicle (1) into the shot image, simultaneously extending all points in the shot image onto the flight track, and forming interception points on the flight track
And step two, calculating the distance between the truncation points in the step one to obtain a truncation line set, and calculating the median of the length of the truncation line in the truncation line set, wherein the median is used as the truncation distance.
4. A forest fire monitoring system based on adaptive clustering of density peaks as claimed in claim 3 wherein: the fire alarm prediction system (4) analyzes and calculates the clustering result in the following specific steps:
step three, after the clustering result is received, circling each cluster, and calculating the number of places in each cluster of the clustering result;
selecting clusters with a large number of places as fire pre-fire rings, sending a signal to an unmanned aerial vehicle control system (2), controlling the shooting unmanned aerial vehicle (1) to move to the position of the fire pre-fire rings by the unmanned aerial vehicle control system (2), and shooting specific images of the fire pre-fire rings by descending the height;
and step five, analyzing whether the specific image obtained in the step four has a flame image or not through an image recognition algorithm, outputting a fire early warning signal if the specific image has the flame image, outputting a fire attention signal if the specific image does not have the flame image, and simultaneously dispatching the fire detection robot to move to a fire pre-distribution ring for detection.
5. A forest fire monitoring system based on density peak adaptive clustering as claimed in claim 4, wherein: the image recognition algorithm in the fifth step recognizes the flame image, and the specific steps are as follows:
fifthly, traversing each color in the specific image and drawing a color boundary;
identifying the red color boundary in the fifth step, amplifying the internal image of the color boundary, and analyzing the pixels of the internal image of the color boundary;
and step three, comparing the image pixel value obtained in the step two with the flame pixel value on the existing network, judging as a flame image if the comparison results are similar, and judging as a non-flame image if the comparison results are different.
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| CN115200722A (en) * | 2022-09-16 | 2022-10-18 | 江苏宸洋食品有限公司 | Temperature measuring method and refrigerator car temperature measuring system applying same |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115200722A (en) * | 2022-09-16 | 2022-10-18 | 江苏宸洋食品有限公司 | Temperature measuring method and refrigerator car temperature measuring system applying same |
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Application publication date: 20210112 |