CN110889327A - Intelligent detection method for sewage draining exit around water area based on thermal infrared image - Google Patents
Intelligent detection method for sewage draining exit around water area based on thermal infrared image Download PDFInfo
- Publication number
- CN110889327A CN110889327A CN201910983757.XA CN201910983757A CN110889327A CN 110889327 A CN110889327 A CN 110889327A CN 201910983757 A CN201910983757 A CN 201910983757A CN 110889327 A CN110889327 A CN 110889327A
- Authority
- CN
- China
- Prior art keywords
- thermal infrared
- image
- pixel
- infrared image
- temperature
- 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.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
- G06V20/13—Satellite images
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformation in the plane of the image
- G06T3/40—Scaling the whole image or part thereof
- G06T3/4038—Scaling the whole image or part thereof for image mosaicing, i.e. plane images composed of plane sub-images
-
- 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]
- G06V10/464—Salient features, e.g. scale invariant feature transforms [SIFT] using a plurality of salient features, e.g. bag-of-words [BoW] representations
Abstract
The invention discloses an intelligent detection method of a sewage outlet at the periphery of a water area based on a thermal infrared image, which comprises the steps of building an unmanned aerial vehicle airborne imaging equipment system and collecting thermal infrared image data; extracting homonymous feature points in an overlapping area between the thermal infrared images; calculating splicing parameters of the large images, and performing geographic parameter registration with the existing high-definition map; calculating the temperature estimation value of each pixel of the whole image, performing threshold segmentation on the thermal infrared spliced image according to the pixel temperature value, and extracting an abnormal pixel area of a temperature field; calculating the average temperature of the pixels in each partition area, and extracting a water body pixel area with the temperature difference value larger than a certain threshold value in the water area; and positioning the extracted sewage outlet pixels to a geographical coordinate system to obtain the geographical position of the sewage outlet. The invention has wide observation range, accurate positioning precision and high data processing automation efficiency, and can be applied to the potential sewage discharge outlet investigation operation around various water areas.
Description
Technical Field
The invention belongs to the technical field of environmental monitoring, and particularly relates to an intelligent detection method for a sewage draining exit at the periphery of a water area based on a thermal infrared image.
Background
The rapid monitoring of the water quality of natural waters is of great importance to the conservation of water resources and related land resources. Today, the discharge of industrial wastewater and domestic sewage is a major cause of water quality pollution. Some sewage draining outlets which do not meet the requirements of the laws and regulations are usually concealed, and the sewage draining period is unstable, such as the phenomenon of avoiding the drainage in daytime, which increases the difficulty of the inspection and the cleaning. In order to meet the requirement of protecting the water environment, the environment protection department needs to check the illegal and unreasonable sewage draining outlets, and then support data is provided for the renovation work.
The existing drain port detection method mainly depends on-site manual investigation of a pull net type, and workers can search by walking on foot or riding a monitoring ship along the shore. The method has the following disadvantages: (1) the manual search has low working efficiency and large workload for large-scale detection and investigation; (2) for the condition that the sewage draining exit is hidden under water or in weeds, the condition is difficult to find in time and easy to miss; (3) the environment of working on the coastal mudflat has certain personal danger to the working personnel.
In addition, partial related work utilizes the satellite remote sensing image to detect the sewage draining exit, and workers find out the potential position of the sewage draining exit on the satellite image by a visual interpretation method and detect the sewage draining exit by combining field comparison. However, the method relying on the satellite remote sensing image has partial problems: (1) the satellite image generally has certain timeliness, the data updating period is slow, and the monitoring is not timely; (2) the resolution of the satellite remote sensing image is usually not high, the low-resolution image cannot accurately reflect the topographic features of the ground features, and the situation of misjudgment and missed judgment can occur; (3) high resolution satellite images, such as sub-meter level data, are typically expensive and increase operating costs.
Disclosure of Invention
The purpose of the invention is as follows: the intelligent detection method for the sewage outlets around the water area based on the thermal infrared image, provided by the invention, has the advantages of flexibility in lifting operation, wide observation range, accurate positioning precision and high data processing automation efficiency, and can be applied to the potential sewage outlet investigation operation around various water areas.
The invention content is as follows: the invention relates to a method for intelligently detecting sewage outlets around a water area based on a thermal infrared image, which comprises the following steps of:
(1) an unmanned aerial vehicle airborne imaging equipment system is set up, and camera parameters of the thermal infrared imager are calibrated by using calibration equipment on the ground;
(2) arranging a flight plan according to the pre-collected flight environment information, executing a flight task, and collecting thermal infrared image data;
(3) extracting homonymous feature points between the thermal infrared images with the overlapping regions;
(4) calculating the splicing parameters of the large image, and carrying out image registration splicing of the large area;
(5) geographic parameter registration, so that data carried by the unmanned aerial vehicle is converted to a geographic coordinate system frame where the existing high-definition map is located;
(6) calculating the temperature estimation value of each pixel of the spliced large image;
(7) carrying out threshold segmentation on the spliced large image according to the temperature value of the pixel;
(8) calculating the average temperature of the pixels in each partition area, and extracting a water body pixel area with the temperature difference value larger than a certain threshold value in the water area, wherein the water body pixel area is an extracted candidate sewage outlet position area;
(9) and positioning the extracted sewage outlet pixels to a geographic coordinate system to obtain the geographic position of the sewage outlet, and performing multiplication operation on the pixel number and the resolution ratio according to the statistics of the pixel number to obtain an estimated value of the primary influence range of the sewage outlet.
Further, the airborne imaging equipment system in the step (1) comprises a thermal infrared imager and a positioning device, and a timestamp for triggering the imager to take a picture is bound with the positioning data.
Further, the thermal infrared image acquisition process in the step (1) is as follows:
taking pictures according to the frequency between 3 frames and 30 frames per second to obtain a thermal infrared image data sequence of an observation area, and storing the thermal infrared image data sequence in an onboard memory card;
the arrangement between the thermal infrared image acquisition intervals t meets the following conditions:
wherein
The thermal infrared image is a thermal infrared image, v is the flight speed of the unmanned aerial vehicle, h is the flight height, and the overlapping degree between two adjacent thermal infrared images is not less than 30%.
Further, the step (4) comprises the steps of:
(41) the image is projected onto the original cylinder surface by the following formula:
where f is the focal length of the image, xcAnd ycIs the central pixel coordinate of the image, (x, y) and (x ', y') are the coordinates of the pixel before and after projection, respectively;
(42) according to computer vision theory, for two images taken by a camera, the homogeneous coordinates of matching points can be related by a homography matrix H, for a pair of corresponding matching points p ═ xp,yp,1]TAnd q ═ xq,yq,1]THaving the relationship: p is Hq;
(43) and splicing the new images to the previously spliced images one by a progressive projection conversion method, thereby finally obtaining a large global spliced image.
Further, the step (5) includes the steps of:
(51) using an existing high-definition map, such as a topographic map or a satellite image of an observation area, and adopting an interactive point selection method to select corresponding matching points with the same name on the thermal infrared large image which is registered and spliced and the existing high-definition map;
(52) and obtaining a conversion relation of mapping the spliced large thermal infrared image to the existing high-definition map by using a homography matrix calculation method again, so that the data carried by the unmanned aerial vehicle is converted into a geographic coordinate system frame where the existing high-definition map is located.
Further, the step (7) includes the steps of:
(71) constructing a temperature histogram of the large image, wherein the histogram has a plurality of peak values, and finding a threshold value for temperature segmentation by adopting a multi-parabola fitting method;
(72) carrying out linear fitting by adopting the individual temperature value actually measured on the ground and the brightness value on the image, and establishing a fitting equation of the brightness value and the temperature value: t isp＝aIp+ b, wherein TpTemperature value, I, representing the position of pixel point ppAnd the image brightness value of the pixel point p is represented, and a and b represent linear fitting parameters obtained by fitting the ground measured temperature value and the brightness value of the corresponding pixel selected on the thermal infrared mosaic large image.
Has the advantages that: compared with the prior art, the invention has the beneficial effects that: 1. by utilizing the portable characteristic of the unmanned aerial vehicle, the operation can be flexibly carried out, the operation cost is reduced for the drain outlet detection work, and the time efficiency is improved; 2. the radiation inversion characteristic of thermal infrared remote sensing is utilized to improve the detection capability of the hidden sewage draining outlet; 3. by combining positioning data and a registration method, the detected sewage outlet image pixel data is converted into position coordinate data, so that accurate sewage outlet position information can be provided; 4. by utilizing the mode identification calculation method, the invention can segment different types of pixels of the water body, thereby calculating the initial diffusion range area of the sewage.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a raw thermal infrared image obtained by an onboard imaging device system;
FIG. 3 is a diagram of matching correspondence between the homonymous feature points extracted from two images during image stitching;
FIG. 4 is a thermal infrared mosaic image obtained by mosaicing thermal infrared images of the entire observation area according to the calculated registration parameters;
FIG. 5 is a temperature diagram calculated by conversion of brightness values to temperature values;
FIG. 6 is a schematic diagram of histogram thresholding based on temperature values;
FIG. 7 is a diagram of candidate drain location areas extracted through threshold segmentation and comparative analysis of the respective areas.
Detailed Description
The technical scheme adopted by the invention is as follows: the utility model provides a thermal infrared image processing system based on unmanned aerial vehicle carries, can detect the peripheral drain information in waters, and the system flow is as shown in figure 1, and concrete step is as follows:
the method comprises the following steps: and (4) building an unmanned aerial vehicle-mounted imaging equipment system. A rotary wing type or fixed wing type unmanned aerial vehicle is used as a flying carrier, a thermal infrared imager is equipped, and the imager is connected to the carrier through a damping ball and a three-axis stable holder. The shock absorbing ball and the three-axis stabilizing pan-tilt are provided by the unmanned aerial vehicle supplier or purchased by oneself. Then, camera parameters of the imager are calibrated on the ground, and camera internal parameters of the thermal infrared image including a focal length f and a central pixel x are obtained by utilizing a checkerboard calibration methodcAnd ycAnd a lens distortion parameter. Meanwhile, the unmanned aerial vehicle carries a global positioning system positioning device (such as a GPS or Beidou positioning module). Binding a timestamp triggering the imager to shoot with positioning data to provide positioning parameters of the imaging time of the image, and storing the thermal infrared image shot in the flight process by using a built-in memory card of the imager. As shown in fig. 2, a thermal infrared image shot by an unmanned aerial vehicle is shown, and it can be observed that a single thermal infrared image without registration splicing can only reflect a scene in a small area; in addition, a certain degree of overlap between adjacent images can be seen, which provides a necessary basis for searching the homonymous points for registration and splicing.
Step two: environmental information acquired by data is investigated, route planning is prepared, the unmanned aerial vehicle performs flight operation, a flight task is executed, and thermal infrared image data are acquired.
The method comprises the steps of collecting weather conditions and terrain information in advance, avoiding carrying out data acquisition in the time under severe weather conditions, avoiding potential dangerous objects which can cause flight safety to the unmanned aerial vehicle, determining air route planning information and providing necessary preparation information for unmanned aerial vehicle flight field data acquisition. The unmanned aerial vehicle carries equipment flight once and can cover 20 square kilometers, and the spatial resolution can be controlled between 5 and 10 centimeters.
And starting field collection, carrying equipment by using an unmanned aerial vehicle, flying according to a path planned by a flight path, and taking pictures according to the frequency between 3 frames and 30 frames per second, wherein the resolution of the pictures depends on the supplier of an imager and can be the picture amplitude above 640 multiplied by 480 pixels in width. And acquiring a thermal infrared image data sequence of the observation area, and storing the thermal infrared image data sequence in an onboard memory card. According to the first step, the onboard positioning data associated with the image is recorded in the memory card.
To ensure that there is more than 50% overlap between two adjacent thermal infrared images, the field angle of the thermal infrared images is known
The following conditions are satisfied by the setting of the flying speed v, the flying height h and the thermal infrared image acquisition interval t of the unmanned aerial vehicle:
the overlapping degree between two adjacent thermal infrared images can be set to be more than 50%, and is recommended to be not less than 30%, otherwise, image splicing of the global observation area is affected.
Step three: the homonymous feature points between the thermal infrared images with the overlap are extracted. The acquisition of the image data is carried out continuously, and the thermal infrared images in the observation range need to be registered and spliced in order to obtain a complete large image of the observation region. According to the first step, the imager and the global positioning data are synchronized by time stamp, the downloaded image has the onboard positioning parameters provided by the positioning module, and the coordinate value is the initial position of the image. In the overlapping area between adjacent images, according to the feature matching technology in the image processing method, the same-name pixels between the overlapping images can be found by utilizing the feature extraction such as SIFT, SURF, Harris, FAST and the like and combining the feature matching method of correlation coefficient calculation, wherein the wrong matching corresponding points are extracted by combining the random Sample consensus (random Sample consensus) method, and the automatic feature point matching method can also be replaced by the manually interactive selected coordinate pixel points. As shown in fig. 3, a matching correspondence relationship between feature points of the same name in a pair of images is given, where pixels connected by a connecting line are feature points corresponding to matching.
Step four: and calculating the splicing parameters of the large images, and performing registration splicing on the thermal infrared images of the whole observation area to obtain the thermal infrared spliced large images. First, to prevent the stitched image from being greatly distorted, the image is projected onto the original cylinder surface by the following formula:
wherein, as stated in step one, f is the focal length of the image, xcAnd ycIs the center pixel coordinate of the image. (x, y) and (x ', y') are coordinates of pixels before projection and after projection, respectively, and projection calculation is performed pixel by pixel.
According to computer vision theory, for two images taken by a camera, the homogeneous coordinates of matching points can be related by a homography matrix H, so that for a pair of corresponding matching points p ═ xp,yp,1]TAnd q ═ xq,yq,1]THaving the relationship: p is Hq.
The homography matrix H is a 3 row 3 column matrix,
when the overlap region has more than 4 sets of homonymous matching points, the homography matrix H can be calculated using a Direct Linear Transformation (DLT) method. With the homography conversion matrix between every two images, new images are spliced to the images spliced before one by a progressive projection conversion method, so that a large global spliced image is finally obtained. And according to the calculated registration parameters, splicing the thermal infrared images of the whole observation area to obtain a thermal infrared spliced large image, wherein brightness values are recorded in all pixels, and the spliced large image is shown in fig. 4.
Step five: and geographic parameter registration, so that the data carried by the unmanned aerial vehicle is converted into a geographic coordinate system frame where the existing high-definition map is located. After the large images are spliced, the existing high-definition map, such as a topographic map or a satellite image of an observation area, is used, an interactive point selection method is adopted, matching points corresponding to the same name are selected on the thermal infrared large images which are registered and spliced and the existing geographical reference high-definition map, and then a homography matrix calculation method is used again to obtain a conversion relation which is mapped from the spliced thermal infrared large images to the existing geographical reference map, so that the data of the unmanned aerial vehicle is converted into the geographical coordinate system frame where the existing map is located. Therefore, each pixel of the registered and spliced thermal infrared large image has a geographic position coordinate attribute, each pixel corresponds to a spatial range of the earth surface, and the resolution mathematical symbol is recorded as s.
Step six: and calculating the temperature estimation value of each pixel of the spliced large image. The specific radiance of the water physically represents the capability of the water to radiate electromagnetic waves outwards, and the temperature difference inevitably exists between the industrial wastewater and the domestic sewage and the natural water into which the industrial wastewater and the domestic sewage are discharged, so that the specific radiance is reflected on the electromagnetic wave radiation capability. The regions of different temperature response exhibit different luminance values across the image.
Before the brightness value is converted into the temperature value, the whole image is subjected to brightness value smoothing by using a Gaussian smoothing template in the image processing technology, so that the influence caused by observation noise is effectively reduced. The energy received by the thermal infrared sensor mainly comprises three parts: earth surface heat radiation after atmosphere weakening; surface reflection of atmospheric downlink radiation; the atmosphere radiates upward. For the following two reasons, the first method focuses mainly on the relative temperature difference, not the absolute temperature value; the flight altitude of the second unmanned aerial vehicle is usually several hundred meters, the task execution time is selected under good meteorological conditions, the atmospheric environment is relatively stable, and therefore the atmospheric influence in the whole observation area can be generally analyzed by using a constant value. Therefore, the method adopts a method of carrying out linear fitting on the individual temperature value actually measured on the ground and the brightness value on the image, establishes a fitting equation of the brightness value and the temperature value, and realizes the calculation from the brightness value to the temperature value by taking the equation as the basis. The linear fit equation is of the form:
Tp＝aIp+b
wherein T ispTemperature value, I, representing the position of pixel point ppAnd a and b represent linear conversion parameters of linear fitting established by the ground measured temperature value and the brightness value on the correspondingly selected thermal infrared splicing large image. The solution of the linear fitting parameters can be easily calculated directly by many calculation software such as ORIGIN or Matlab. After the fitting parameters are provided, each pixel of the whole image is calculated according to a linear fitting equation to obtain the temperature value of each pixel. As shown in fig. 5, the stitched large image becomes a large image for temperature display, and at this time, the value recorded by each pixel is a temperature value.
Step seven: and carrying out segmentation threshold segmentation on the thermal infrared spliced image based on temperature values, and extracting abnormal pixel areas of the temperature field. The contaminated water body area may be understood as a target area and the uncontaminated area as a background area. The temperature values of the pixels in each area have the characteristic of being relatively consistent, the relative temperature values of the target area and the background area present certain differentiation, the threshold segmentation can be carried out by utilizing a histogram statistics method based on the temperature values, and the background area and the target area are segmented. A temperature histogram of a large image is constructed, as shown in fig. 6, the histogram typically having a plurality of peaks. And finding a threshold value of the brightness segmentation by adopting a multi-parabola fitting method. The pixels corresponding to the temperature values within the threshold interval are divided into one area.
Step eight: and combining the results of the step six and the step seven, and analyzing the temperature characteristics in each divided area by taking the divided area of the step seven as a constraint. First, an average temperature value within the region is calculated. And then, comparing the temperatures of the adjacent areas, and extracting an independent area of which the temperature difference is greater than or less than that of all the adjacent areas, wherein the area is the extracted candidate sewage outlet position area. As shown in fig. 7, after the region segmentation and the region comparison analysis, a region temperature at the lower right corner of the image is obviously different from the temperature of the peripheral region, and is highlighted by the color deepening as indicated by the arrow. Therefore, the area is extracted as a candidate drain area, and a verification confirmation of the candidate is waited.
Step nine: and fifthly, according to the position and resolution information of each pixel, the extracted candidate sewage draining exit pixel can be positioned in a geographic coordinate system, and therefore the geographic position coordinate of the candidate sewage draining exit is output. And according to the statistics of the number of pixels in the area after threshold segmentation, the area estimation value of the primary influence range of the candidate sewage draining exit on the surrounding water area is obtained by multiplying the number of pixels and the pixel spatial resolution.
Step ten: ground workers adopt instruments such as a water quality sampler, a multi-parameter water quality tester, a turbidity tester, a non-dispersive infrared oil tester and the like, go to the site according to positioning results, perform site discrimination on candidate sewage outlets, and detect and verify whether the sewage outlets exist or not.
Claims (6)
1. A method for intelligently detecting sewage outlets around a water area based on a thermal infrared image is characterized by comprising the following steps:
(1) an unmanned aerial vehicle airborne imaging equipment system is set up, and camera parameters of the thermal infrared imager are calibrated by using calibration equipment on the ground;
(2) arranging a flight plan according to the pre-collected flight environment information, executing a flight task, and collecting thermal infrared image data;
(3) extracting homonymous feature points between the thermal infrared images with the overlapping regions;
(4) calculating the splicing parameters of the large image, and carrying out image registration splicing of the large area;
(5) geographic parameter registration, so that data carried by the unmanned aerial vehicle is converted to a geographic coordinate system frame where the existing high-definition map is located;
(6) calculating the temperature estimation value of each pixel of the spliced large image;
(7) carrying out threshold segmentation on the spliced large image according to the temperature value of the pixel;
(8) calculating the average temperature of the pixels in each partition area, and extracting a water body pixel area with the temperature difference value larger than a certain threshold value in the water area, wherein the water body pixel area is an extracted candidate sewage outlet position area;
(9) and positioning the extracted sewage outlet pixels to a geographic coordinate system to obtain the geographic position of the sewage outlet, and performing multiplication operation on the pixel number and the resolution ratio according to the statistics of the pixel number to obtain an estimated value of the primary influence range of the sewage outlet.
2. The intelligent detection method for sewage outfalls around a water area based on thermal infrared image as claimed in claim 1 wherein said airborne imaging equipment system of step (1) comprises a thermal infrared imager and a positioning device, and binds a timestamp triggering the imager to take a picture with positioning data.
3. The intelligent detection method for sewage outfalls around a water area based on the thermal infrared image as claimed in claim 1, wherein the thermal infrared image acquisition process in step (1) is as follows:
taking pictures according to the frequency between 3 frames and 30 frames per second to obtain a thermal infrared image data sequence of an observation area, and storing the thermal infrared image data sequence in an onboard memory card;
the arrangement between the thermal infrared image acquisition intervals t meets the following conditions:
wherein
The field angle of the thermal infrared image is v, the flight speed of the unmanned aerial vehicle is h, the flight height is h, and the weight between two adjacent thermal infrared imagesThe folding degree is not less than 30%.
4. The intelligent detection method for sewage outfalls around a water area based on the thermal infrared image as claimed in claim 1, wherein the step (4) comprises the following steps:
(41) the image is projected onto the original cylinder surface by the following formula:
where f is the focal length of the image, xcAnd ycIs the central pixel coordinate of the image, (x, y) and (x ', y') are the coordinates of the pixel before and after projection, respectively;
(42) according to computer vision theory, for two images taken by a camera, the homogeneous coordinates of matching points can be related by a homography matrix H, for a pair of corresponding matching points p ═ xp,yp,1]TAnd q ═ xq,yq,1]THaving the relationship: p is Hq;
(43) and splicing the new images to the previously spliced images one by a progressive projection conversion method, thereby finally obtaining a large global spliced image.
5. The intelligent detection method for sewage outfalls around a water area based on the thermal infrared image as claimed in claim 1, wherein the step (5) comprises the following steps:
(51) using an existing high-definition map, such as a topographic map or a satellite image of an observation area, and adopting an interactive point selection method to select corresponding matching points with the same name on the thermal infrared large image which is registered and spliced and the existing high-definition map;
(52) and obtaining a conversion relation of mapping the spliced large thermal infrared image to the existing high-definition map by using a homography matrix calculation method again, so that the data carried by the unmanned aerial vehicle is converted into a geographic coordinate system frame where the existing high-definition map is located.
6. The intelligent detection method for sewage outfalls around a water area based on the thermal infrared image as claimed in claim 1, wherein the step (7) comprises the following steps:
(71) constructing a temperature histogram of the large image, wherein the histogram has a plurality of peak values, and finding a threshold value for temperature segmentation by adopting a multi-parabola fitting method;
(72) carrying out linear fitting by adopting the individual temperature value actually measured on the ground and the brightness value on the image, and establishing a fitting equation of the brightness value and the temperature value: t isp＝aIp+ b, wherein TpTemperature value, I, representing the position of pixel point ppAnd the image brightness value of the pixel point p is represented, and a and b represent linear fitting parameters obtained by fitting the ground measured temperature value and the brightness value of the corresponding pixel selected on the thermal infrared mosaic large image.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910983757.XA CN110889327B (en) | 2019-10-16 | 2019-10-16 | Intelligent detection method for sewage outlet around water area based on thermal infrared image |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910983757.XA CN110889327B (en) | 2019-10-16 | 2019-10-16 | Intelligent detection method for sewage outlet around water area based on thermal infrared image |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110889327A true CN110889327A (en) | 2020-03-17 |
| CN110889327B CN110889327B (en) | 2023-05-26 |
Family
ID=69746220
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910983757.XA Active CN110889327B (en) | 2019-10-16 | 2019-10-16 | Intelligent detection method for sewage outlet around water area based on thermal infrared image |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110889327B (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112485329A (en) * | 2020-11-27 | 2021-03-12 | 重庆商勤科技有限公司 | Method, device and system for detecting sewage draining outlet based on combination of thermal imaging and ultrasound |
| CN112987751A (en) * | 2021-03-18 | 2021-06-18 | 南京理工大学 | System and method for quickly detecting hidden sewage draining outlet in automatic cruising mode |
| CN113011368A (en) * | 2021-03-31 | 2021-06-22 | 神华神东煤炭集团有限责任公司 | Mine mining surface conduction goaf crack identification method and electronic equipment |
| CN113786134A (en) * | 2021-09-27 | 2021-12-14 | 汤恩智能科技(上海)有限公司 | Cleaning method, program product, readable medium and electronic device |
| CN114359731A (en) * | 2022-01-05 | 2022-04-15 | 浙江中科空间信息技术应用研发中心 | Sewage hidden discharge detection traceability method based on unmanned aerial vehicle thermal infrared remote sensing |
| CN114371268A (en) * | 2022-01-10 | 2022-04-19 | 江苏大学 | Suspension type self-balancing self-cruising water quality on-line monitoring device and monitoring and evaluating method |
| CN114515663A (en) * | 2022-01-11 | 2022-05-20 | 北京市农林科学院信息技术研究中心 | Mushroom spraying control method, device and system |
| CN113011368B (en) * | 2021-03-31 | 2024-04-30 | 神华神东煤炭集团有限责任公司 | Mine exploitation surface conduction goaf crack identification method and electronic equipment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102088569A (en) * | 2010-10-13 | 2011-06-08 | 首都师范大学 | Sequence image splicing method and system of low-altitude unmanned vehicle |
| CN106485655A (en) * | 2015-09-01 | 2017-03-08 | 张长隆 | A kind of taken photo by plane map generation system and method based on quadrotor |
| CN107843939A (en) * | 2017-10-24 | 2018-03-27 | 防灾科技学院 | Coal fire recognition methods based on unmanned plane thermal infrared imagery |
| CN109523544A (en) * | 2018-11-26 | 2019-03-26 | 陕西汉通建设工程质量检测有限公司 | A kind of external wall mass defect detection system and its method |
| CN109978862A (en) * | 2019-03-27 | 2019-07-05 | 北京邮电大学 | A kind of air pollution estimation method based on satellite image |
-
2019
- 2019-10-16 CN CN201910983757.XA patent/CN110889327B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102088569A (en) * | 2010-10-13 | 2011-06-08 | 首都师范大学 | Sequence image splicing method and system of low-altitude unmanned vehicle |
| CN106485655A (en) * | 2015-09-01 | 2017-03-08 | 张长隆 | A kind of taken photo by plane map generation system and method based on quadrotor |
| CN107843939A (en) * | 2017-10-24 | 2018-03-27 | 防灾科技学院 | Coal fire recognition methods based on unmanned plane thermal infrared imagery |
| CN109523544A (en) * | 2018-11-26 | 2019-03-26 | 陕西汉通建设工程质量检测有限公司 | A kind of external wall mass defect detection system and its method |
| CN109978862A (en) * | 2019-03-27 | 2019-07-05 | 北京邮电大学 | A kind of air pollution estimation method based on satellite image |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112485329A (en) * | 2020-11-27 | 2021-03-12 | 重庆商勤科技有限公司 | Method, device and system for detecting sewage draining outlet based on combination of thermal imaging and ultrasound |
| CN112485329B (en) * | 2020-11-27 | 2024-01-26 | 重庆商勤科技有限公司 | Method, device and system for detecting sewage outlet based on combination of thermal imaging and ultrasound |
| CN112987751A (en) * | 2021-03-18 | 2021-06-18 | 南京理工大学 | System and method for quickly detecting hidden sewage draining outlet in automatic cruising mode |
| CN112987751B (en) * | 2021-03-18 | 2022-10-11 | 中冶华天工程技术有限公司 | System and method for quickly detecting hidden sewage draining outlet in automatic cruising mode |
| CN113011368A (en) * | 2021-03-31 | 2021-06-22 | 神华神东煤炭集团有限责任公司 | Mine mining surface conduction goaf crack identification method and electronic equipment |
| CN113011368B (en) * | 2021-03-31 | 2024-04-30 | 神华神东煤炭集团有限责任公司 | Mine exploitation surface conduction goaf crack identification method and electronic equipment |
| CN113786134A (en) * | 2021-09-27 | 2021-12-14 | 汤恩智能科技(上海)有限公司 | Cleaning method, program product, readable medium and electronic device |
| CN114359731A (en) * | 2022-01-05 | 2022-04-15 | 浙江中科空间信息技术应用研发中心 | Sewage hidden discharge detection traceability method based on unmanned aerial vehicle thermal infrared remote sensing |
| CN114371268A (en) * | 2022-01-10 | 2022-04-19 | 江苏大学 | Suspension type self-balancing self-cruising water quality on-line monitoring device and monitoring and evaluating method |
| CN114515663A (en) * | 2022-01-11 | 2022-05-20 | 北京市农林科学院信息技术研究中心 | Mushroom spraying control method, device and system |
| CN114515663B (en) * | 2022-01-11 | 2023-01-31 | 北京市农林科学院信息技术研究中心 | Mushroom spraying control method, device and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110889327B (en) | 2023-05-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110889327B (en) | Intelligent detection method for sewage outlet around water area based on thermal infrared image | |
| WO2021093283A1 (en) | Sea surface small-area oil spill region detection system and detection method based on multi-sensing fusion | |
| CN105516584B (en) | Full-view image acquisition system, the device and method for measuring skyline based on it | |
| CN105527969B (en) | A kind of mountain garden belt investigation and monitoring method based on unmanned plane | |
| Kako et al. | Low altitude remote-sensing method to monitor marine and beach litter of various colors using a balloon equipped with a digital camera | |
| Ngoc et al. | Coastal and inland water pixels extraction algorithm (WiPE) from spectral shape analysis and HSV transformation applied to Landsat 8 OLI and Sentinel-2 MSI | |
| CN103625649B (en) | A kind of aircraft independent landing area judging method | |
| Friman et al. | Methods for large-scale monitoring of district heating systems using airborne thermography | |
| CN110703244B (en) | Method and device for identifying urban water body based on remote sensing data | |
| KR101874498B1 (en) | System and Method for Aerial Photogrammetry of Ground Control Point for Space Information Acquisition based on Unmanned Aerial Vehicle System | |
| Gerhardinger et al. | Vehicles detection from very high resolution satellite imagery | |
| CN112308913B (en) | Vehicle positioning method and device based on vision and vehicle-mounted terminal | |
| CN111426637A (en) | Method for acquiring water quality parameters of urban black and odorous rivers by using unmanned aerial vehicle | |
| CN109596121A (en) | A kind of motor-driven station Automatic Targets and space-location method | |
| CN111692978A (en) | Multi-source image data-based illegal building identification method | |
| CN110413002B (en) | Unmanned aerial vehicle remote sensing information acquisition method and device | |
| Taherzadeh et al. | Using hyperspectral remote sensing data in urban mapping over Kuala Lumpur | |
| Schwalbe et al. | Hemispheric image modeling and analysis techniques for solar radiation determination in forest ecosystems | |
| Fiorillo et al. | Automated Mapping of the roof damage in historic buildings in seismic areas with UAV photogrammetry | |
| CN112166688B (en) | Method for monitoring desert and desertification land based on minisatellite | |
| CN107301658A (en) | A kind of method that unmanned plane image is positioned with extensive old times phase image Rapid matching | |
| Lehrbass et al. | Urban tree cover mapping with relief-corrected aerial imagery and LiDAR | |
| Wang et al. | Performance tests for automatic 3D geometric data registration technique for progressive as-built construction site modeling | |
| Ning et al. | Detecting new building construction in urban areas based on images of small unmanned aerial system | |
| CN110136104A (en) | Image processing method, system and medium based on unmanned aerial vehicle station |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |