Spatial dust concentration detection method based on mixed algorithm of pattern recognition and infrared thermal imaging

Abstract

The present application relates to a kind of space dust concentration detection methods of pattern recognition and infrared thermal imaging hybrid algorithm, belong to dust concentration detection field.The method includes the following steps: on the basis of optical image dust concentration identification method, introduce infrared thermal imaging, utilize infrared thermal imaging separation and identify the distribution of background and dust particle, distinguish and identify water mist on optical image, realize full-area dust concentration detection in spray or high fog environment;For the shortcoming that existing image dust concentration identification cannot effectively distinguish water mist and dust, introduce infrared thermal imaging technology, combined with separation algorithm, realize full-space dust concentration detection in spray environment.

Description

Technical Field

[0001] The present invention belongs to the field of dust concentration detection and relates to a spatial dust concentration detection method using a hybrid algorithm of pattern recognition and infrared thermal imaging. Background Art

[0002] Currently, there are numerous mines, serious dust hazards, and a high incidence of pneumoconiosis. Furthermore, underground dust-generating points in mines are numerous and dispersed. Current dust concentration detection methods focus on single points, and dust concentration detection in surface environments based on image recognition technology is still in its infancy.

[0003] Regarding the issue of the impact of image dust concentration recognition technology on moisture in the environment, existing technologies do have some limitations, especially in high-water environments such as underground coal mines and in spray dust reduction areas, where this impact is more obvious.

[0004] Existing dust concentration identification technology is mainly based on optical images. The advantages of this method are simple equipment and fast measurement speed. However, for moisture in the environment, especially in high-water environments, changes in image reflectivity may make it difficult to distinguish between dust and moisture.

[0005] To address this issue, researchers are experimenting with more sophisticated image processing techniques and machine learning algorithms, such as a dust concentration measurement algorithm based on image transmittance. This technique analyzes the transmittance characteristics of an image and combines them with other image information, such as color and brightness, to distinguish between dust and moisture. While this technology has addressed the issue to some extent, further research and improvement may be needed in certain environments and conditions, such as those with high water content.

[0006] In addition, some researchers are trying to use more advanced image recognition technologies, such as deep learning, to distinguish between dust and moisture. 8 This technology can better understand and interpret complex patterns and structures in images and may provide a more effective way to distinguish between dust and moisture in the future.

[0007] In general, existing image dust concentration recognition technology faces certain challenges when dealing with dust in high-water environments and spray dust precipitation areas, but researchers are exploring and developing better methods to improve the accuracy and reliability of dust identification.

[0008] Existing image dust concentration recognition technology basically only considers dust concentration measurement based on the optical image itself. This method cannot effectively distinguish the interference of water in the environment on dust concentration. Coal mines are high-water environments, especially in areas where spray dust suppression is carried out. A single optical image cannot distinguish between spray and particulate matter. Summary of the Invention

[0009] In light of this, the present invention aims to provide a method for detecting spatial dust concentration using a hybrid algorithm combining pattern recognition and infrared thermal imaging. Infrared thermal imaging combined with optical imaging can effectively remove ambient mist. Combined with optical imaging to measure total dust concentration, this method enables rapid, full-space dust concentration detection. This technology is particularly well-suited for high-mist environments such as those encountered in underground working surfaces, where spray dust suppression is common, effectively eliminating the impact of mist on image recognition technology.

[0010] In order to achieve the above object, the present invention provides the following technical solutions:

[0011] A method for detecting spatial dust concentration using a hybrid algorithm of pattern recognition and infrared thermal imaging, the method comprising the following steps:

[0012] Based on the optical image dust concentration identification method, infrared thermal imaging is introduced to separate and identify the background and the distribution of dust particles using infrared thermal imaging, and to distinguish and identify water mist on the optical image, thus realizing full-area dust concentration detection in spray or high fog environments;

[0013] The optical image dust concentration recognition method is specifically as follows:

[0014] Assume that for any point x in space, the optical image RGB value and the corresponding coordinates are the matrix formula I(x), the infrared thermal image is R(x), and in a clean environment without dust and water mist, the background image is J(x). Solve for the dust concentration value C;

[0015] The water mist recognition algorithm of infrared thermal imaging obtained through learning and training is F[J(x), R(x)] = r. The obtained value of the influence factor of fog particles on the transmittance of the image is A(x) based on the ambient light influence expression solved by the original optical image dust concentration recognition algorithm. Then the transmittance value of the dust image corresponding to point x is c(x) solved by the following formula (1):

[0016] I(x)=J(x)*[1-c(x)-r]+A(c(x)+r)(1)

[0017] The dust concentration value C is solved based on the corresponding relationship between the original dust concentration and the image transmittance;

[0018] The method of using infrared thermal imaging to separate and identify the distribution of background and dust particles is as follows:

[0019] In dust concentration detection, infrared thermal imaging technology uses the thermal radiation characteristics of dust particles to distinguish the thermal radiation intensity of dust particles from the background environment, thereby identifying and locating dust particles; then, by analyzing the thermal radiation intensity of dust, dust concentration information is obtained.

[0020] Optionally, the infrared thermal imaging technology is to collect the thermal radiation information of the dust particles by an infrared thermal imaging device, convert the thermal radiation information into images and graphics for human visual recognition, analyze the thermal radiation characteristics of the dust, and infer the physical and chemical properties of the dust to realize the detection of the dust concentration.

[0021] Optionally, the thermal radiation information includes the thermal radiation characteristics of the dust particles, the thermal radiation intensity of the dust particles, and the radiation temperature of the dust particles.

[0022] The thermal radiation characteristics of the dust particles are reflected in the thermal radiation intensity and the radiation temperature of the dust particles.

[0023] The thermal radiation intensity of the dust particles is related to the particle size, shape and physical properties of the dust particles.

[0024] The radiation temperature of the dust particles is related to the particle size, shape and physical properties of the dust particles.

[0025] Optionally, the spatial dust concentration detection method is applied in a spraying or high fog environment.

[0026] The present application has the advantages that, in view of the fact that the existing image dust concentration recognition cannot effectively distinguish water mist and dust, the infrared thermal imaging technology is introduced, combined with a separation algorithm, to realize the detection of the dust concentration in the whole space in a spraying environment.

[0027] Other advantages, objects and features of the present application will be clarified in the following description, and will be apparent to those skilled in the art based on the following description, or can be taught from the practice of the present application. The objects and other advantages of the present application can be achieved and obtained by the following description. BRIEF DESCRIPTION OF DRAWINGS

[0028] In order to make the objects, technical solutions and advantages of the present application clearer, the preferred detailed description of the present application will be given below with reference to the accompanying drawings, in which:

[0029] Figure 1 The flowchart of the present application. DETAILED DESCRIPTION

[0030] The embodiments of the present application will be described below by specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the disclosure. The present application can also be implemented or applied by different specific embodiments, and the details in the description can be modified or changed based on different views and applications without departing from the spirit of the present application. It should be noted that the diagrams provided in the following examples only illustrate the basic concept of the present application in a schematic manner, and the following examples and features in the examples can be combined with each other without conflict.

[0031] Among them, the accompanying drawings are only for illustrative purposes and represent only schematic diagrams rather than actual pictures, and should not be understood as limiting the present invention. In order to better illustrate the embodiments of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the dimensions of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions may be omitted in the accompanying drawings.

[0032] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", "front", "back", etc. indicating directions or positional relationships, they are based on the directions or positional relationships shown in the drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operate in a specific direction. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0033] See also Figure 1 , which is a spatial dust concentration detection method based on a hybrid algorithm of pattern recognition and infrared thermal imaging.

[0034] Based on the method of optical image dust concentration identification, infrared thermal imaging is introduced, and an algorithm is designed to use infrared thermal imaging to separate and identify the background and the distribution of dust particles, so as to distinguish and identify water mist in the optical image, and achieve the purpose of full-area dust concentration detection in spray or high fog environments.

[0035] (1) Method of optical image dust concentration recognition

[0036] Assume that for any point x in space, the optical image RGB value and the corresponding coordinates are the matrix formula I(x), the infrared thermal image is R(x), and in a clean environment without dust and water mist, the background image is J(x). Solve for the dust concentration value C;

[0037] The water mist recognition algorithm of infrared thermal imaging obtained through learning and training is F[J(x), R(x)] = r. The obtained value of the influence factor of fog particles on the transmittance of the image is A(x) based on the ambient light influence expression solved by the original optical image dust concentration recognition algorithm. Then the transmittance value of the dust image corresponding to point x is c(x) solved by the following formula (1):

[0038] I(x)=J(x)*[1-c(x)-r]+A(c(x)+r)(1)

[0039] Then, the dust concentration value C is solved based on the corresponding relationship between the original dust concentration and the image transmittance.

[0040] The main innovation of this algorithm lies in the machine learning and process of F[J(x), R(x)] = r, as well as the adjustment and learning of formula (1) based on the original optical image dust concentration recognition algorithm formula for water mist and dust particles, one is the refraction of light, the other is the diffuse reflection and occlusion of light.

[0041] Optical image dust concentration identification relies primarily on image processing technology. A common method is a dust concentration measurement algorithm based on image transmittance. This algorithm extracts the transmittance characteristics of dust images and calculates the transmittance value based on image saturation and brightness information. Ultimately, it establishes a mapping relationship between dust concentration and image transmittance, achieving efficient and high-precision dust concentration measurement.

[0042] Specifically, the algorithm first establishes a dust concentration visual measurement experimental platform, collects dust images, and then extracts the transmittance characteristics of the dust images. Then, based on dark channel theory, the transmittance of the dust images is calculated by combining image saturation and brightness information. Finally, a polynomial fitting method is used to establish a mapping relationship between dust concentration and image transmittance, thereby achieving efficient and high-precision dust concentration measurement.

[0043] Research results demonstrate that the algorithm not only effectively measures dust concentration, but also achieves an average relative error of only 7.77%, significantly improving accuracy and extending the measurement range. This image transmittance-based dust concentration identification method has been widely used in practical applications, such as real-time online monitoring of dust concentration generated during the loading, unloading, and transportation of particulate materials.

[0044] (2) Application of infrared thermal imaging in dust concentration detection

[0045] Infrared thermal imaging technology is a technology that receives the infrared radiation energy of the target through an infrared detector, then reflects it to the photosensitive element of the infrared detector to obtain an infrared thermal image and display the temperature distribution of the object surface in different colors.

[0046] In dust concentration detection, infrared thermal imaging technology can use the thermal radiation characteristics of dust particles to distinguish the thermal radiation intensity of dust particles from the background environment, thereby achieving dust particle identification and location. Then, by analyzing the thermal radiation intensity of dust, dust concentration information can be obtained.

[0047] Infrared thermal imaging technology can indeed utilize the thermal radiation characteristics of dust particles to detect dust concentration. The basic principle of this technology is to use infrared thermal imaging equipment to collect thermal radiation information from dust particles and convert it into images and graphics that can be visually distinguished by humans. Because the thermal radiation characteristics of dust particles are affected by factors such as their physical and chemical properties, particle size, and shape, analyzing the thermal radiation characteristics of dust can infer its physical and chemical properties, thereby enabling dust concentration detection.

[0048] Specifically, the thermal radiation characteristics of dust particles are primarily reflected in their thermal radiation intensity and radiation temperature. The thermal radiation intensity of dust particles is related to factors such as their particle size, shape, and physical properties, while the radiation temperature is related to factors such as particle size, shape, and physical properties. In actual testing, infrared thermal imaging equipment can accurately capture the thermal radiation intensity and radiation temperature of dust particles. Through data processing and analysis, it can derive the physical and chemical properties of the dust and, in turn, infer its concentration.

[0049] In addition, the application of infrared thermal imaging technology in dust concentration detection also has the advantages of non-contact, no interference, fast response speed, and high measurement accuracy. In some special environments, such as high temperature and high dust concentration, infrared thermal imaging technology can also be effectively applied and provide new possibilities for dust concentration detection.

[0050] Specifically, when the temperature of dust particles is higher than the background environment, infrared thermal imaging technology can "light up" the thermal radiation intensity of the dust particles and display it on the thermal image. By comparing the difference between the dust thermal image and the background thermal image, the thermal radiation intensity of the dust particles can be obtained and further converted into dust concentration.

[0051] Implementation Method 1

[0052] Optical image dust concentration identification method:

[0053] Assume that at a certain point x in the space, the optical image RGB value is [255, 150, 100], and the corresponding coordinates are (10, 20).

[0054] The temperature at this point obtained by infrared thermal imaging equipment is 25℃.

[0055] In a dust-free and water mist-free environment, the RGB value of the background image is [250, 140, 90].

[0056] The trained infrared thermal imaging water mist recognition algorithm outputs a value of 0.1 as the factor affecting the transmittance of fog particles on the image.

[0057] Calculate the transmittance value of the dust image at point x according to formula (1):

[0058] Ambient light impact expression: A(x) = (255-250) / 255 = 0.02

[0059] Dust image transmittance value: T(x) = 0.9*0.9*(1-0.02)*(1-0.1) = 0.78

[0060] According to the established mapping relationship between dust concentration and image transmittance, the dust concentration at point x is calculated to be 5 mg / m 3 .

[0061] Infrared thermal imaging:

[0062] The infrared thermal imaging device obtains the temperature at this point as 25°C, and the background ambient temperature is 20°C.

[0063] Since the temperature of dust particles is higher than the background environment, this point appears as an obvious high-temperature area on the infrared thermal image.

[0064] Combined with optical image analysis, it was confirmed that the high-temperature area was dust particles, not water mist or background.

[0065] In practical applications, this method can realize dust concentration detection in the entire area under high dust concentration environments, such as spray or high fog environments, thereby providing important data support for dust control and safe production.

[0066] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. A spatial dust concentration detection method using a hybrid algorithm of pattern recognition and infrared thermal imaging, characterized by: The method comprises the following steps: Based on the optical image dust concentration identification method, infrared thermal imaging is introduced to separate and identify the background and the distribution of dust particles using infrared thermal imaging, and to distinguish and identify water mist on the optical image, thus realizing full-area dust concentration detection in spray or high fog environments; The optical image dust concentration recognition method is specifically as follows: Assume that for any point in space x Point, optical image RGB value and corresponding coordinates are matrix formula , infrared thermal imaging is , in a clean environment without dust and water mist, the background image is , solve the dust concentration value C ; The water mist recognition algorithm of infrared thermal imaging obtained through learning and training, The obtained value of the influence factor of fog particles on the transmittance of the image is expressed as follows: Then the corresponding x The dust image transmittance value of the point is Solve it using the following formula (1): （1） The dust concentration value is solved based on the corresponding relationship between the original dust concentration and the image transmittance C ; The method of using infrared thermal imaging to separate and identify the distribution of background and dust particles is as follows: In dust concentration detection, infrared thermal imaging technology uses the thermal radiation characteristics of dust particles to distinguish the thermal radiation intensity of dust particles from the background environment, thereby identifying and locating dust particles; then, by analyzing the thermal radiation intensity of dust, dust concentration information is obtained.

2. The spatial dust concentration detection method using a hybrid algorithm of pattern recognition and infrared thermal imaging according to claim 1 is characterized by: The infrared thermal imaging technology collects the thermal radiation information of dust particles through infrared thermal imaging equipment and converts it into images and graphics for human visual identification; by analyzing the thermal radiation characteristics of dust, its physical and chemical properties are inferred to achieve dust concentration detection.

3. The spatial dust concentration detection method using a hybrid algorithm of pattern recognition and infrared thermal imaging according to claim 2 is characterized by: The thermal radiation information includes the thermal radiation characteristics of the dust particles, the thermal radiation intensity of the dust particles and the radiation temperature of the dust particles; The thermal radiation characteristics of the dust particles are reflected in their thermal radiation intensity and radiation temperature; The thermal radiation intensity of the dust particles is related to their particle size, shape and physical properties; The radiation temperature of the dust particles is related to their particle size, shape and physical properties.

4. The spatial dust concentration detection method using a hybrid algorithm of pattern recognition and infrared thermal imaging according to claim 3 is characterized by: The spatial dust concentration detection method is applied in a spray or high fog environment.

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