CN204166149U - Forest smoke region detection system - Google Patents
Forest smoke region detection system Download PDFInfo
- Publication number
- CN204166149U CN204166149U CN201420654053.0U CN201420654053U CN204166149U CN 204166149 U CN204166149 U CN 204166149U CN 201420654053 U CN201420654053 U CN 201420654053U CN 204166149 U CN204166149 U CN 204166149U
- Authority
- CN
- China
- Prior art keywords
- smog
- smoke
- forest
- color channel
- communication interface
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
Landscapes
- Radar Systems Or Details Thereof (AREA)
Abstract
The utility model relates to a kind of forest smoke region detection system, described detection system is positioned on unmanned plane, comprise microwave communication interface, smoke detection apparatus, Unmanned Aerial Vehicle Powerplants and master controller, the flight steering order that described microwave communication interface sends for receiving far-end forest management platform, described flight steering order comprises detection position, described master controller and described microwave communication interface, described smoke detection apparatus is connected respectively with described Unmanned Aerial Vehicle Powerplants, control described Unmanned Aerial Vehicle Powerplants to fly to described detection position, and when determining described unmanned plane in described detection position, start described smoke detection apparatus to detect the smoke region in forest.By the utility model, accurately can determine the particular location that whether there is smog and smog in monitored wood land, be convenient to forest management department and respond fast, avoid the further expansion of the condition of a disaster.
Description
Technical field
The utility model relates to forest protection field, particularly relates to a kind of forest smoke region detection system.
Background technology
Forest fire is the formidable enemy jeopardized forests, and a fire just can turn to dust and ashes large stretches of forests asking of this morning or evening, causes serious personnel and property loss.Find point of origin as early as possible and he eliminated in the budding stage to be the effective way controlling fire hazard, to have great importance.
Before fire occurs and when fire occurs, all can produce with a large amount of smog, before fire, smog produces the most outstanding, and therefore, Smoke Detection can be used for realizing Forest Fire Alarm.Because combustion phenomena mainly comprises flame and smog, when burning occurs, combustion phenomena and comburant have much relations, and comburant is different, and combustion phenomena has very large difference, as produced the size of smog, the color etc. of smog.Thus, the color characteristic studying smog phenomenon in the forest map picture gathered is the important step of warning algorithm research.
Current forest fire monitoring has that observatory is observed, aircraft patrols and the mode such as satellite infrared image monitoring, and first method efficiency is low, dangerous high; Although a kind of last method has many inherent advantages, if not by the restriction of the geographical conditions such as topography and geomorphology, occupy a commanding position, monitoring wide coverage, cost of investment is high, implements complicated.Therefore, aircraft patrols, especially advantage of lower cost, use safer unmanned plane forest fire control in there is wider range of application.
But, Smoke Detection mode of the prior art is comparatively simple, recognition efficiency is not high, therefore, a kind of Forest Fire Alarm mode be arranged on unmanned plane newly of current needs, by the mode of Remote, handle any crucial place being easy to breaking out of fire in unmanned plane to forest and perform Smoke Detection, by the preinvasive smog of detection of fires, judge position and fire the present situation of breaking out of fire, for the forest management department of far-end provides valuable reference data
Utility model content
In order to solve the problem, the utility model provides a kind of forest smoke region detection system, detection system is built on unmanned plane, utilize unmanned plane flexible, detection faces is broad and be easy to the technical characterstic of remote control, control each position execution Smoke Detection that unmanned plane flies in forest, and by smog Iamge Segmentation, the pattern of Smoke Detection by different level that smoke region is determined, determine whether detect position exists smog, whether there is the condition of a fire, and the actual footprint area size of smog, thus safety, reliably for the decision-making of forest management department is offered help.
According to one side of the present utility model, provide a kind of forest smoke region detection system, described detection system comprises microwave communication interface, smoke detection apparatus, Unmanned Aerial Vehicle Powerplants and master controller, the flight steering order that described microwave communication interface sends for receiving far-end forest management platform, described flight steering order comprises detection position, described master controller and described microwave communication interface, described smoke detection apparatus is connected respectively with described Unmanned Aerial Vehicle Powerplants, control described Unmanned Aerial Vehicle Powerplants to fly to described detection position, and when determining described unmanned plane in described detection position, start described smoke detection apparatus to detect the smoke region in forest.
More specifically, in the detection system of described forest smoke region, described detection system also comprises, usb communication interface, be connected with random access memory, for automatically reading in described random access memory by the data in outside USB flash disk, the data in described outside USB flash disk comprise smog upper limit gray threshold, smog lower limit gray threshold, RGB average threshold value, RGB standard deviation threshold method and saturation degree threshold value, random access memory, for storing described smog upper limit gray threshold, described smog lower limit gray threshold, described RGB average threshold value, RGB standard deviation threshold method and saturation degree threshold value, the Big Dipper positioning equipment, connects the Big Dipper Navsat, for receiving real-time the Big Dipper data of described unmanned plane position, radio altitude detector, comprise radio transmitter, radio receiver and microcontroller, described microcontroller is connected respectively with described radio transmitter and described radio receiver, described radio transmitter launches radiowave earthward, described radio receiver receives the radiowave of ground return, described microcontroller calculates the present level of described unmanned plane according to the launch time of described radio transmitter, the time of reception of described radio receiver and velocity of radio wave, and described velocity of radio wave is the light velocity, realize microwave communication between described microwave communication interface with described far-end forest management platform to be connected, the detection position in the described flight steering order received comprises target flight height and target flight the Big Dipper data, described Unmanned Aerial Vehicle Powerplants comprises rotary piston engine, flies to described detection position for driving described unmanned plane, described smoke detection apparatus comprises interconnective aerial camera and smog image processor, described aerial camera is linear array digital aviation video camera, comprise undercarriage having shock absorption function, front cover glass, camera lens, filter and image-forming electron unit, for taking place, described detection position scale Forest Scene to export forest map picture, described smog image processor comprises pretreatment unit, cutting unit and recognition unit, described pretreatment unit is connected with described aerial camera, to carry out noise reduction and filtering process successively to the forest map picture received, output filtering image, described cutting unit is connected respectively with described pretreatment unit and described random access memory, for the pixel identification of gray-scale value in described filtering image between described smog upper limit gray threshold and described smog lower limit gray threshold is formed doubtful smog subimage, described recognition unit is connected respectively with described cutting unit and described random access memory, for identifying the smoke region in described doubtful smog subimage, described master controller is the dsp chip TMS320C6416 of TI company, with described the Big Dipper positioning equipment, described radio altitude detector, described microwave communication interface, described Unmanned Aerial Vehicle Powerplants is connected respectively with described smoke detection apparatus, when the present level of described unmanned plane with described target flight matched and described real-time the Big Dipper data are mated with described target flight the Big Dipper time, start the aerial camera in described smoke detection apparatus and smog image processor, afterwards, described master controller is when receiving smoke region and there is smoke signal, by described forest map picture, described smoke region and describedly there is smoke signal and be transmitted to described far-end forest management platform by described microwave communication interface, when receiving non smoke signal, described non smoke signal is transmitted to described far-end forest management platform by described microwave communication interface, wherein, described smog upper limit gray threshold and described smog lower limit gray threshold are used for the doubtful smog in image and background separation, and described RGB average threshold value, described RGB standard deviation threshold method and described saturation degree threshold value are used for determining the smoke region in doubtful smog further, described recognition unit is for each the doubtful smog pixel in described doubtful smog subimage, calculate RGB average, RGB standard deviation and saturation degree, if the RGB average calculated is less than or equal to described RGB average threshold value, the RGB standard deviation calculated is less than or equal to described RGB standard deviation threshold method and the saturation degree calculated is less than or equal to described saturation degree threshold value, then determine that this doubtful smog pixel is smog pixel, otherwise, then determine that this doubtful smog pixel is non-smog pixel, when there is not smog pixel in described doubtful smog subimage, send non smoke signal, when there is smog pixel in described doubtful smog subimage, based on the composition smoke region, position of all smog pixels in described doubtful smog subimage, and send and there is smoke signal.
More specifically, in the detection system of described forest smoke region, described usb communication interface, described random access memory, described the Big Dipper positioning equipment and described master controller are all positioned at the front end panel board of described unmanned plane.
More specifically, in the detection system of described forest smoke region, described usb communication interface, described random access memory, described the Big Dipper positioning equipment and described master controller are integrated on one piece of surface-mounted integrated circuit.
More specifically, in the detection system of described forest smoke region, described smoke detection apparatus is positioned at immediately below described unmanned plane shell, described radio altitude detector and described microwave communication interface are embedded in the front-end surface of described unmanned plane shell, and described Unmanned Aerial Vehicle Powerplants is encapsulated in the front end of described unmanned plane shell.
More specifically, in the detection system of described forest smoke region, described detection system also comprises, power-supply unit, for powering for described detection system.
More specifically, in the detection system of described forest smoke region, the standard deviation that described RGB average is R color channel values, G color channel values and the mean value of B color channel values, described RGB standard deviation are R color channel values, G color channel values and B color channel values, described saturation degree is that the difference of maximal value and minimum value in R color channel values, G color channel values and B color channel values is divided by maximal value in R color channel values, G color channel values and B color channel values.
Accompanying drawing explanation
Below with reference to accompanying drawing, embodiment of the present utility model is described, wherein:
Fig. 1 is the block diagram of the forest smoke region detection system according to the utility model embodiment.
Fig. 2 is the block diagram of the smoke detection apparatus of forest smoke region detection system according to the utility model embodiment.
Embodiment
Below with reference to accompanying drawings the embodiment of forest smoke region of the present utility model detection system is described in detail.
Fire occurs frequent and has destructive disaster, not only can cause huge property loss, but also the life security of entail dangers to people, if just can identify at fire early period of origination.Then can reduce various loss.
Fire in generating process, usual supporter, the essential characteristics such as high temperature, high heat, high light, intense radiation.Current fire detecting system all adopts traditional temperature-sensitive, sensor such as sense cigarette, photoelectricity etc., detection of fires is carried out respectively based on the temperature of fire, the flue gas of generation, flame spectrum feature, but this mode efficiency is low, security is not high, and this system can not grasp the situation of scene of fire, it cannot be relied on to carry out Real-Time Scheduling and process.Also have a kind of mode to be monitor forest condition by the mode of satellite monitoring, but this mode expends huge, cost performance is not high.At present, the mode cost performance that patrols of aircraft is the most applicable.
The smog produced in forest fire also exists color distribution rule in the picture.Forest fire early period of origination, because trees have certain wetness, therefore burn not cmpletely, this will produce a large amount of smog, if crown fire, smog horizon can reach 1500 meters, and the generation of smog will produce significantly impact to video image.Therefore, be the important component part of forest fire identification to the identification of smog, and because smog generally produces prior to flame, the identification of smog contribute to the early warning of forest fire.
By the smog color feature that statistics forest fire produces, sum up the statistical law of smog, set up the statistical model of smog, people can be helped to adopt the means of image recognition, from the forest map picture gathered, locate smoke region fast.But currently used Smoke Detection mode just analyzes the gray-scale value size of smog image simply, at image pixel in smog intensity value ranges, just regards as smog pixel, this mode is too simple, and precision is not high.
Forest smoke region of the present utility model detection system, the unmanned aerial vehicle that can control exactly to cruise flies to any position of wishing key monitoring in forest, by image recognition technology and double layer smog region detection technology, judge whether smog exists and the size of smog exactly, thus provide early warning mechanism for forest fire.
Fig. 1 is the block diagram of the forest smoke region detection system according to the utility model embodiment, and described detection system comprises usb communication interface 1, random access memory 2, the Big Dipper positioning equipment 3, radio altitude detector 4, master controller 5, microwave communication interface 6, Unmanned Aerial Vehicle Powerplants 7, smoke detection apparatus 8 and power-supply unit 9, the flight steering order that described microwave communication interface 6 sends for receiving far-end forest management platform, described flight steering order comprises detection position, described master controller 5 and described the Big Dipper positioning equipment 3, described radio altitude detector 4, described microwave communication interface 6, described Unmanned Aerial Vehicle Powerplants 7 is connected respectively with described smoke detection apparatus 8, control described Unmanned Aerial Vehicle Powerplants 7 to fly to described detection position, and when determining described unmanned plane in described detection position, start described smoke detection apparatus 8 to detect the smoke region in forest, described master controller 5 can also with described usb communication interface 1, random access memory 2 is connected respectively with power-supply unit 9, with to described usb communication interface 1, random access memory 2 and power-supply unit 9 provide corresponding control signal respectively.
Then, the concrete structure of forest smoke region of the present utility model detection system is further detailed.
Described usb communication interface 1 is connected with random access memory 2, for the data in outside USB flash disk automatically being read in described random access memory 2, the data in described outside USB flash disk comprise smog upper limit gray threshold, smog lower limit gray threshold, RGB average threshold value, RGB standard deviation threshold method and saturation degree threshold value.Preferably, described RGB average threshold value can value be 220, and described RGB standard deviation threshold method can value be 20, and described saturation degree threshold value can value be 0.1.
Described random access memory 2 is for storing described smog upper limit gray threshold, described smog lower limit gray threshold, described RGB average threshold value, RGB standard deviation threshold method and saturation degree threshold value.
Described the Big Dipper positioning equipment 3, connects the Big Dipper Navsat, for receiving real-time the Big Dipper data of described unmanned plane position.
Described radio altitude detector 4, comprise radio transmitter, radio receiver and microcontroller, described microcontroller is connected respectively with described radio transmitter and described radio receiver, described radio transmitter launches radiowave earthward, described radio receiver receives the radiowave of ground return, described microcontroller calculates the present level of described unmanned plane according to the launch time of described radio transmitter, the time of reception of described radio receiver and velocity of radio wave, and described velocity of radio wave is the light velocity.
Realize microwave communication between described microwave communication interface 6 with described far-end forest management platform to be connected, the detection position in the described flight steering order received comprises target flight height and target flight the Big Dipper data.
Described Unmanned Aerial Vehicle Powerplants 7 comprises rotary piston engine, flies to described detection position for driving described unmanned plane.
As shown in Figure 2, described smoke detection apparatus 8 comprises interconnective aerial camera 81 and smog image processor 82, described aerial camera 81 is linear array digital aviation video camera, comprise undercarriage having shock absorption function, front cover glass, camera lens, filter and image-forming electron unit, for taking place, described detection position scale Forest Scene to export forest map picture, described smog image processor 82 comprises pretreatment unit, cutting unit and recognition unit.
In described smog image processor 82, described pretreatment unit is connected with described aerial camera 81, to carry out noise reduction and filtering process successively to the forest map picture received, output filtering image, described cutting unit is connected respectively with described pretreatment unit and described random access memory 2, for the pixel identification of gray-scale value in described filtering image between described smog upper limit gray threshold and described smog lower limit gray threshold is formed doubtful smog subimage, described recognition unit is connected respectively with described cutting unit and described random access memory 2, for identifying the smoke region in described doubtful smog subimage.
Described master controller 5 is the dsp chip TMS320C6416 of TI company, when the present level of described unmanned plane with described target flight matched and described real-time the Big Dipper data are mated with described target flight the Big Dipper time, start the aerial camera 81 in described smoke detection apparatus 8 and smog image processor 82, afterwards, described master controller 5 is when receiving smoke region and there is smoke signal, by described forest map picture, described smoke region and describedly there is smoke signal and be transmitted to described far-end forest management platform by described microwave communication interface 6, when receiving non smoke signal, described non smoke signal is transmitted to described far-end forest management platform by described microwave communication interface 6.
Wherein, described smog upper limit gray threshold and described smog lower limit gray threshold are used for the doubtful smog in image and background separation, and described RGB average threshold value, described RGB standard deviation threshold method and described saturation degree threshold value are used for determining the smoke region in doubtful smog further, described recognition unit is for each the doubtful smog pixel in described doubtful smog subimage, calculate RGB average, RGB standard deviation and saturation degree, if the RGB average calculated is less than or equal to described RGB average threshold value, the RGB standard deviation calculated is less than or equal to described RGB standard deviation threshold method and the saturation degree calculated is less than or equal to described saturation degree threshold value, then determine that this doubtful smog pixel is smog pixel, otherwise, then determine that this doubtful smog pixel is non-smog pixel, when there is not smog pixel in described doubtful smog subimage, send non smoke signal, when there is smog pixel in described doubtful smog subimage, based on the composition smoke region, position of all smog pixels in described doubtful smog subimage, and send and there is smoke signal.
Wherein, in the detection system of described forest smoke region, described usb communication interface 1, described random access memory 2, described the Big Dipper positioning equipment 3 and described master controller 5 are all positioned at the front end panel board of described unmanned plane, and can be integrated on one piece of surface-mounted integrated circuit, described smoke detection apparatus 8 can be positioned at immediately below described unmanned plane shell, described radio altitude detector 4 and described microwave communication interface 6 can be embedded in the front-end surface of described unmanned plane shell, described Unmanned Aerial Vehicle Powerplants 7 can be encapsulated in the front end of described unmanned plane shell, described power-supply unit 9 is for powering for described detection system, described RGB average is R color channel values, the mean value of G color channel values and B color channel values, described RGB standard deviation is R color channel values, the standard deviation of G color channel values and B color channel values, described saturation degree is R color channel values, in G color channel values and B color channel values, the difference of maximal value and minimum value is divided by R color channel values, maximal value in G color channel values and B color channel values.
In addition, in described smoke detection apparatus 82, described pretreatment unit is connected with described aerial camera 81, to carry out noise reduction and filtering process successively to the forest map picture received, output filtering image, wherein, noise reduction process and filtering process are two kinds of different process, the target frequency composition removed is different, noise reduction process may be used for removing the salt-pepper noise in forest map picture, white Gaussian noise etc., to obtain noise-reduced image, and filtering process can be used for carrying out low-pass filtering to described noise-reduced image, wavelet filtering, auto adapted filtering, medium filtering etc., to obtain described filtering image.
In addition, image is made up of multiple pixel, the pixel value of each pixel can represent with the R color channel values of RGB color space, G color channel values and B color channel values, R color channel values is red color channel value, G color channel values is green color channel value, and B color channel values is blue color channels value.Described RGB average is that R color channel values, G color channel values and B color channel values are added divided by 3, described RGB standard deviation be by the difference of R color channel values and described RGB average square, the difference of G color channel values and described RGB average square, after the summed square of the difference of B color channel values and described RGB average divided by 3, obtained by the value evolution obtained, described saturation degree is that the difference of maximal value and minimum value in R color channel values, G color channel values and B color channel values is divided by maximal value in R color channel values, G color channel values and B color channel values.
Adopt forest smoke region of the present utility model detection system, for the technical matters that detection carrier is indefinite, Smoke Detection precision is not high of existing forest smoke detection system, adopt cost performance moderate, control more flexibly unmanned plane as detection carrier, introduce with different levels smoke region detecting pattern, add detection efficiency, improve accuracy of detection, the prevention for forest fire provides better early warning means.
Be understandable that, although the utility model with preferred embodiment disclose as above, but above-described embodiment and be not used to limit the utility model.For any those of ordinary skill in the art, do not departing under technical solutions of the utility model ambit, the technology contents of above-mentioned announcement all can be utilized to make many possible variations and modification to technical solutions of the utility model, or be revised as the Equivalent embodiments of equivalent variations.Therefore, every content not departing from technical solutions of the utility model, according to technical spirit of the present utility model to any simple modification made for any of the above embodiments, equivalent variations and modification, all still belongs in the scope of technical solutions of the utility model protection.
Claims (7)
1. a forest smoke region detection system, described detection system is positioned on unmanned plane, it is characterized in that, described detection system comprises microwave communication interface, smoke detection apparatus, Unmanned Aerial Vehicle Powerplants and master controller, the flight steering order that described microwave communication interface sends for receiving far-end forest management platform, described flight steering order comprises detection position, described master controller and described microwave communication interface, described smoke detection apparatus is connected respectively with described Unmanned Aerial Vehicle Powerplants, control described Unmanned Aerial Vehicle Powerplants to fly to described detection position, and when determining described unmanned plane in described detection position, start described smoke detection apparatus to detect the smoke region in forest.
2. forest smoke region as claimed in claim 1 detection system, it is characterized in that, described detection system also comprises:
Usb communication interface, be connected with random access memory, for automatically reading in described random access memory by the data in outside USB flash disk, the data in described outside USB flash disk comprise smog upper limit gray threshold, smog lower limit gray threshold, RGB average threshold value, RGB standard deviation threshold method and saturation degree threshold value;
Random access memory, for storing described smog upper limit gray threshold, described smog lower limit gray threshold, described RGB average threshold value, RGB standard deviation threshold method and saturation degree threshold value;
The Big Dipper positioning equipment, connects the Big Dipper Navsat, for receiving real-time the Big Dipper data of described unmanned plane position;
Radio altitude detector, comprise radio transmitter, radio receiver and microcontroller, described microcontroller is connected respectively with described radio transmitter and described radio receiver, described radio transmitter launches radiowave earthward, described radio receiver receives the radiowave of ground return, described microcontroller calculates the present level of described unmanned plane according to the launch time of described radio transmitter, the time of reception of described radio receiver and velocity of radio wave, and described velocity of radio wave is the light velocity;
Realize microwave communication between described microwave communication interface with described far-end forest management platform to be connected, the detection position in the described flight steering order received comprises target flight height and target flight the Big Dipper data;
Described Unmanned Aerial Vehicle Powerplants comprises rotary piston engine, flies to described detection position for driving described unmanned plane;
Described smoke detection apparatus comprises interconnective aerial camera and smog image processor, described aerial camera is linear array digital aviation video camera, comprise undercarriage having shock absorption function, front cover glass, camera lens, filter and image-forming electron unit, for taking place, described detection position scale Forest Scene to export forest map picture, described smog image processor comprises pretreatment unit, cutting unit and recognition unit, described pretreatment unit is connected with described aerial camera, to carry out noise reduction and filtering process successively to the forest map picture received, output filtering image, described cutting unit is connected respectively with described pretreatment unit and described random access memory, for the pixel identification of gray-scale value in described filtering image between described smog upper limit gray threshold and described smog lower limit gray threshold is formed doubtful smog subimage, described recognition unit is connected respectively with described cutting unit and described random access memory, for identifying the smoke region in described doubtful smog subimage,
Described master controller is the dsp chip TMS320C6416 of TI company, with described the Big Dipper positioning equipment, described radio altitude detector, described microwave communication interface, described Unmanned Aerial Vehicle Powerplants is connected respectively with described smoke detection apparatus, when the present level of described unmanned plane with described target flight matched and described real-time the Big Dipper data are mated with described target flight the Big Dipper time, start the aerial camera in described smoke detection apparatus and smog image processor, afterwards, described master controller is when receiving smoke region and there is smoke signal, by described forest map picture, described smoke region and describedly there is smoke signal and be transmitted to described far-end forest management platform by described microwave communication interface, when receiving non smoke signal, described non smoke signal is transmitted to described far-end forest management platform by described microwave communication interface,
Wherein, described smog upper limit gray threshold and described smog lower limit gray threshold are used for the doubtful smog in image and background separation, and described RGB average threshold value, described RGB standard deviation threshold method and described saturation degree threshold value are used for determining the smoke region in doubtful smog further;
Wherein, described recognition unit is for each the doubtful smog pixel in described doubtful smog subimage, calculate RGB average, RGB standard deviation and saturation degree, if the RGB average calculated is less than or equal to described RGB average threshold value, the RGB standard deviation calculated is less than or equal to described RGB standard deviation threshold method and the saturation degree calculated is less than or equal to described saturation degree threshold value, then determine that this doubtful smog pixel is smog pixel, otherwise, then determine that this doubtful smog pixel is non-smog pixel, when there is not smog pixel in described doubtful smog subimage, send non smoke signal, when there is smog pixel in described doubtful smog subimage, based on the composition smoke region, position of all smog pixels in described doubtful smog subimage, and send and there is smoke signal.
3. forest smoke region as claimed in claim 2 detection system, is characterized in that:
Described usb communication interface, described random access memory, described the Big Dipper positioning equipment and described master controller are all positioned at the front end panel board of described unmanned plane.
4. forest smoke region as claimed in claim 3 detection system, is characterized in that:
Described usb communication interface, described random access memory, described the Big Dipper positioning equipment and described master controller are integrated on one piece of surface-mounted integrated circuit.
5. forest smoke region as claimed in claim 2 detection system, is characterized in that:
Described smoke detection apparatus is positioned at immediately below described unmanned plane shell, described radio altitude detector and described microwave communication interface are embedded in the front-end surface of described unmanned plane shell, and described Unmanned Aerial Vehicle Powerplants is encapsulated in the front end of described unmanned plane shell.
6. forest smoke region as claimed in claim 2 detection system, it is characterized in that, described detection system also comprises:
Power-supply unit, for powering for described detection system.
7. forest smoke region as claimed in claim 2 detection system, is characterized in that:
The standard deviation that described RGB average is R color channel values, G color channel values and the mean value of B color channel values, described RGB standard deviation are R color channel values, G color channel values and B color channel values, described saturation degree is that the difference of maximal value and minimum value in R color channel values, G color channel values and B color channel values is divided by maximal value in R color channel values, G color channel values and B color channel values.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201420654053.0U CN204166149U (en) | 2014-11-04 | 2014-11-04 | Forest smoke region detection system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201420654053.0U CN204166149U (en) | 2014-11-04 | 2014-11-04 | Forest smoke region detection system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN204166149U true CN204166149U (en) | 2015-02-18 |
Family
ID=52539825
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201420654053.0U Expired - Fee Related CN204166149U (en) | 2014-11-04 | 2014-11-04 | Forest smoke region detection system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN204166149U (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104316974A (en) * | 2014-11-04 | 2015-01-28 | 无锡北斗星通信息科技有限公司 | Forest smoke area detecting system |
| CN107741747A (en) * | 2017-09-25 | 2018-02-27 | 广东工商职业学院 | Automatic forest patrol system and method based on unmanned plane |
| CN112034456A (en) * | 2020-08-27 | 2020-12-04 | 五邑大学 | Smoke inspection system, method, control device and storage medium |
-
2014
- 2014-11-04 CN CN201420654053.0U patent/CN204166149U/en not_active Expired - Fee Related
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104316974A (en) * | 2014-11-04 | 2015-01-28 | 无锡北斗星通信息科技有限公司 | Forest smoke area detecting system |
| CN104316974B (en) * | 2014-11-04 | 2017-03-01 | 青岛橡胶谷知识产权有限公司 | Forest smoke region detecting system |
| CN107741747A (en) * | 2017-09-25 | 2018-02-27 | 广东工商职业学院 | Automatic forest patrol system and method based on unmanned plane |
| CN107741747B (en) * | 2017-09-25 | 2020-10-16 | 广东工商职业学院 | Automatic forest patrol system and method based on unmanned aerial vehicle |
| CN112034456A (en) * | 2020-08-27 | 2020-12-04 | 五邑大学 | Smoke inspection system, method, control device and storage medium |
| CN112034456B (en) * | 2020-08-27 | 2023-10-17 | 五邑大学 | Smoke inspection system, method, control device and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104316974A (en) | Forest smoke area detecting system | |
| CN104732700B (en) | Based on the fire alarm system taken photo by plane in the air | |
| CN109981192B (en) | Frequency spectrum monitoring method of airspace blackout flying unmanned aerial vehicle | |
| CN204166149U (en) | Forest smoke region detection system | |
| CN104599427B (en) | A kind of intelligent image type fire alarm system for vcehicular tunnel | |
| CN108333584A (en) | A kind of remote unmanned plane detection system of low altitude small target and detection method | |
| CN111664752B (en) | Unmanned aerial vehicle counter-braking system | |
| CN101650866A (en) | Fire detecting system applied to unmanned helicopter and fire detecting method thereof | |
| CN104715556B (en) | Based on the condition of a fire alarm method taken photo by plane in the air | |
| CN105511491B (en) | A kind of unmanned plane device for driving away and method | |
| Yang et al. | Early detection of forest fire based on unmaned aerial vehicle platform | |
| CN213716108U (en) | Forest fire prevention early warning detecting system | |
| CN103761827A (en) | Fire monitor system | |
| CN111610538A (en) | Unmanned aerial vehicle navigation decoy system | |
| CN210839737U (en) | Forest fire prevention monitor terminal based on Beidou navigation and positioning | |
| CN106788873A (en) | A kind of unmanned plane interference method and system | |
| CN104735423B (en) | The transmission facility identification platform being positioned on unmanned plane | |
| KR101366198B1 (en) | Image processing method for automatic early smoke signature of forest fire detection based on the gaussian background mixture models and hsl color space analysis | |
| CN115115595A (en) | Real-time calibration method of airborne laser radar and infrared camera for forest fire monitoring | |
| CN103942806B (en) | The detection method of Multi-information acquisition fire ignition point based on high-spectral data | |
| CN109451431B (en) | Navigation and link region rejection method for civil low-slow small unmanned aerial vehicle | |
| CN105208346B (en) | Transmission facility identification method based on unmanned plane | |
| CN205334237U (en) | Unmanned aerial vehicle drives device | |
| Dennison et al. | Wildfire detection for retrieving fire temperature from hyperspectral data | |
| CN114320469A (en) | Cloud-edge intelligence-based underground hazard source detection method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20150218 Termination date: 20151104 |
|
| EXPY | Termination of patent right or utility model |