Shallow coal seam surface gas guide crack identification method based on infrared thermal imaging

Abstract

The invention relates to the technical field of coal seam earth surface gas guide crack recognition, and discloses a shallow coal seam earth surface gas guide crack recognition method based on infrared thermal imaging, which comprises the following steps: step 1, data acquisition: (1) equipment preparation and field preparation are carried out, and thermal imaging data acquisition is carried out on the shallow coal seam earth surface by adopting an infrared thermal imager after the preparation is completed; (2) in the acquisition process, performing comprehensive scanning on the target area according to a preset latticed or strip-shaped path to obtain a high-resolution infrared thermal image sequence, and recording position information and corresponding time information of each acquisition point; according to the shallow coal seam surface gas guide crack identification method based on infrared thermal imaging, (1) by using the characteristics of non-contact, rapidness, no illumination limitation and the like of the infrared thermal imaging technology, the temperature information of the large-area shallow coal seam surface can be efficiently obtained, the crack identification efficiency is greatly improved, and the labor intensity of workers and the risk of field exploration are reduced;

Description

Technical Field

[0001] The present invention relates to the technical field of coal seam surface gas-conducting crack identification, and in particular to a shallow coal seam surface gas-conducting crack identification method based on infrared thermal imaging. Background Art

[0002] Surface gas-conducting cracks in shallow coal seams refer to a series of cracks that appear on the surface due to deformation and damage of the overburden above the coal seams during the mining process of shallow coal seams. The existence of gas-conducting cracks may lead to coalbed methane leakage, thereby affecting safety and the environment. Therefore, it is particularly important to accurately identify and monitor gas-conducting cracks. In the process of coal seam mining, the existing gas-conducting crack detection technologies mainly include the following methods: (1) Manual detection: This method usually requires multiple inspectors to conduct walking inspections on the ground. The inspectors need to have professional knowledge and cannot quickly cover a large area, resulting in a long detection cycle, insufficient ability to detect hidden dangers in a timely manner and low efficiency. The detection results often rely on the experience and judgment of the operators, and there may be omissions and misjudgments, which can easily cause subjective factors to affect objective reality; (2) Geological radar (GPR) technology: Geological radar equipment is expensive to purchase and maintain, and some small coal mines are not suitable for this purpose. Mines or regions with limited resources may not be able to bear such an economic burden, and the data obtained by GPR needs to be processed and analyzed by professionals in a complex manner. This places high demands on technical personnel, requires additional training and expansion of professional knowledge, and increases labor costs; (3) Ground drilling: It will damage the coal seam and the surrounding geological structure, which may cause crack expansion and environmental damage. At the same time, the drilling process may cause damage to the coal seam. It is invasive and destructive. Drilling operations can only obtain samples at specific points and cannot fully evaluate the status of the entire area. It is difficult to reflect the distribution of surface cracks; (4) Gas concentration monitoring technology: Gas concentration monitoring takes a certain amount of time to reflect changes in coalbed methane, may miss the early stages of leakage, and cannot provide the precise location of cracks. The above-mentioned crack detection methods are mostly manual inspections, tunnel monitoring, etc., which are inefficient and have safety hazards.

[0003] Based on this, the present invention provides a method for identifying surface gas-conducting cracks in shallow coal seams based on infrared thermal imaging. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a method for identifying surface gas-conducting cracks in shallow coal seams based on infrared thermal imaging. It has the advantages of using unmanned aerial vehicle remote sensing technology equipped with infrared thermal imaging instruments to scan the surface of shallow coal seams and identify gas-conducting cracks, thereby solving the problems raised in the background technology.

[0005] The present invention provides the following technical solution: a method for identifying surface gas-conducting cracks in shallow coal seams based on infrared thermal imaging, comprising the following steps:

[0006] Step 1: Data collection: (1) including equipment preparation and site preparation. After the preparation is completed, the surface of the shallow coal seam is thermally imaged using an infrared thermal imager to collect thermal imaging data; (2) During the collection process, the target area is fully scanned according to a predetermined grid or strip path to obtain a high-resolution infrared thermal image sequence, and the location information and corresponding time information of each collection point are recorded;

[0007] Step 2: Image preprocessing: Use data processing software to remove noise from the collected thermal imaging images;

[0008] Step 3: Temperature feature extraction: Analyze the preprocessed infrared thermal image and extract the temperature characteristics of the shallow coal seam surface gas-conducting cracks based on the temperature differences between the cracks and the surrounding medium. By setting an appropriate temperature threshold range, the pixels in the image that meet the temperature threshold are extracted as the pixel set of potential crack areas, thereby screening out temperature anomaly areas.

[0009] Step 4: Crack morphology identification: By using the Canny edge detection algorithm plug-in in ImageJ to perform edge detection on the processed thermal imaging image and adaptively adjusting the high and low thresholds, the edges of the cracks can be detected more accurately, the occurrence of false edges can be reduced, and the detected edges can be connected and fitted to obtain the geometric shape and direction information of the cracks, including the length, width, direction and branching of the cracks, and the possible contours of the gas-conducting cracks can be identified;

[0010] Step 5: Crack location and annotation: Combined with the location information recorded during the acquisition process, the identified cracks are located and annotated on the Geographic Information System (GIS) platform to visually display the distribution of cracks on the surface. At the same time, different types of cracks are classified and annotated based on their temperature and morphological characteristics.

[0011] Step 6: Result verification: Select some representative crack areas, measure the actual parameters of the cracks manually on site, compare and analyze them with the infrared thermal imaging recognition results, and then record and report the data.

[0012] Preferably, the thermal imaging data collection needs to be carried out on sunny days and without strong winds, and a suitable collection time should be selected to ensure that the surface temperature field distribution is relatively stable and is not drastically affected by external factors such as solar radiation.

[0013] Preferably, the infrared thermal imager needs to be parallel to the ground and maintain a suitable shooting distance (1.5-3 meters) to achieve the best imaging effect.

[0014] Preferably, the equipment preparation includes:

[0015] (1) Select a suitable drone that can carry infrared thermal imaging equipment;

[0016] (2) Select a suitable infrared thermal imaging instrument, ensuring that it has good resolution and sensitivity within the required temperature range and is equipped with a GPS positioning system;

[0017] (3) Configure a laptop or mobile terminal and install data processing software (Photoshop, ImageJ) for thermal imaging image analysis.

[0018] Preferably, the site preparation includes:

[0019] (1) Determine the monitoring area and set the UAV flight range;

[0020] (2) Understand the coal seam depth, fracture distribution and gas release characteristics in the monitoring area;

[0021] (3) Conduct a site survey to assess the terrain, vegetation, and meteorological conditions in the area, including temperature, humidity, and wind speed.

[0022] Preferably, the temperature feature extraction is performed by setting a suitable temperature threshold range, extracting the pixel points that meet the temperature threshold in the image, and using them as the pixel set of the potential crack area to screen out the temperature abnormality area. The threshold determination formula based on statistical analysis is shown as follows: By collecting infrared thermal imaging data of the normal surface area of ​​the shallow buried coal seam, a series of temperature samples T1, T2, ..., T n , first calculate the mean of these samples and standard deviation σ,

[0023]

[0024] Set the lower limit of temperature abnormality threshold T min and upper limit T max for:

[0025]

[0026] Among them, k is the empirical coefficient, which effectively distinguishes normal and abnormal temperature areas through statistical laws.

[0027] Preferably, the data recording and reporting includes:

[0028] (1) Record the specific location and characteristics of the cracks and save the thermal imaging images and analysis results;

[0029] (2) Prepare detailed monitoring reports, including potential risk assessments and remediation recommendations, such as regular monitoring measures, ventilation improvements, and safe evacuation route planning.

[0030] Preferably, the denoising process first performs grayscale processing to convert the color infrared thermal image into a grayscale image to simplify the subsequent processing process and highlight the temperature information. Then, a Gaussian filter algorithm is used in Photoshop to remove noise points in the image, improve the image clarity and remove image noise interference. The image is converted to Lab color mode, and the "a" or "b" channel is selected in the channel panel. The "Gaussian Blur" filter is applied. By adjusting the blur radius, the noise can be effectively reduced. The same operation is performed on the "lightness" channel, and then the image is converted back to RGB mode. Alternatively, the layer to be processed can be copied, the "Gaussian Blur" filter is applied to the layer, and the blending mode of the blurred layer is set to "Overlay". A black brush is used to remove the parts that do not need to be overlaid. Next, the image is contrast enhanced. By stretching the grayscale value range, the temperature difference in the image is made more obvious, which facilitates the extraction of crack features.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] (1) By utilizing the characteristics of infrared thermal imaging technology such as non-contact, rapid, and not restricted by light, it is possible to efficiently obtain surface temperature information of large areas of shallow coal seams, greatly improving the efficiency of crack identification and reducing the intensity of manual labor and the danger of on-site exploration.

[0033] (2) Through precise preprocessing and feature extraction of infrared thermal images, combined with an improved edge detection algorithm, the shape and location of gas-conducting fractures can be accurately identified, effectively overcoming the problems of traditional methods that are greatly affected by environmental factors and have low accuracy, and providing reliable technical support for gas prevention and control and surface ecological environment protection in the process of shallow coal mining.

[0034] (3) This method locates and classifies cracks on the GIS platform, which facilitates the management, analysis, and visualization of crack information. It helps researchers and engineering technicians to intuitively understand the distribution of cracks and formulate more scientific and reasonable mining plans and disaster prevention measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of the process of the present invention;

[0036] Figure 2 This is a schematic diagram comparing the infrared thermal imaging image of the present invention with the actual photo;

[0037] Figure 3 For the present invention Figure 2 Schematic diagram of the comparison sub-figure. DETAILED DESCRIPTION

[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0039] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0040] Coal seams are a geochemical product formed millions of years ago. The formation of coal seams is a long process involving a variety of complex geological and chemical processes. Coal seams are formed by the accumulation of plant debris. After being buried underground, these plant debris are gradually transformed into coal through chemical reactions and pressure. These plant debris are usually accumulated from a variety of plants such as trees, ferns, and herbs. When these plants die, their remains are usually covered by water and gradually deposited to the bottom. These remains are gradually buried in mud and sand, compacted and protected. Overall, the formation of coal seams is a long and complex process that requires the joint action of multiple geological and chemical factors, including the source of plant debris, changes in the sedimentary environment, changes in geological structure, heat and pressure, etc. Coal seams have played an important role in human history and are one of the main sources of fuel and energy.

[0041] Before starting to mine coal, prospecting work must be carried out first. Prospecting is to understand the distribution of underground coal, the thickness and hardness of coal seams through geological exploration and drilling. This information is very important for planning excavation plans and determining mining methods. During the prospecting process, geologists will collect a large amount of geological data, including the composition, structure, age and physical properties of rocks. These data can help determine the distribution and quality of coal seams. After the distribution of underground coal is ascertained, detailed planning is required. Planning includes determining the excavation route, mining method and equipment to be used. Planners need to consider many factors, such as the thickness, hardness, angle and direction of the coal seams, as well as the safety and economic benefits of miners. Planners need to base their plans on the According to the prospecting data and actual needs, a detailed mining plan is formulated. Coal mining usually adopts two methods: underground mining and open-pit mining. Underground mining is to mine the coal in the coal seam by digging underground tunnels. This process requires the use of various coal mining equipment, such as excavators, loaders, conveyors, etc. During the coal mining process, the mine needs to be ventilated and drained to ensure the safety of miners and smooth production. After the coal is excavated, it needs to be transported to the processing plant or transport vehicle on the ground. During the transportation process, attention should be paid to the loss and quality of coal, and the safety and efficiency of the transport vehicles should be ensured. During the transportation process, the coal also needs to be screened and sorted to ensure its quality and meet customer needs.

[0042] Surface gas-conducting cracks in shallow coal seams refer to a series of cracks that appear on the surface due to deformation and damage of the overburden above the coal seam during the mining process of shallow coal seams. The existence of gas-conducting cracks may lead to coalbed methane leakage, thereby affecting safety and the environment. Therefore, it is particularly important to accurately identify and monitor gas-conducting cracks.

[0043] In the process of coal seam mining, the existing gas-conducting crack detection technologies mainly include the following methods: (1) Manual detection: This method usually requires multiple inspectors to conduct walking inspections on the ground. The inspectors need to have professional knowledge and cannot quickly cover a large area, resulting in a long detection cycle, insufficient ability to detect hidden dangers in a timely manner and low efficiency. The detection results often rely on the experience and judgment of the operators, which may result in omissions and misjudgments, and easily cause subjective factors to affect objective reality; (2) Geological Radar (GPR) technology: Geological radar equipment is expensive to purchase and maintain. Some small coal mines or areas with limited resources may not be able to bear such an economic burden, and the data obtained by GPR needs to be processed and processed by professionals for complex and complicated procedures. Analysis: This places high demands on technical personnel, requiring additional training and expansion of professional knowledge, and increasing labor costs; (3) Ground drilling: It will destroy the coal seam and the surrounding geological structure, which may cause crack expansion and environmental damage. At the same time, the drilling process may cause damage to the coal seam, which is invasive and destructive. Drilling operations can only obtain samples at specific points and cannot fully evaluate the status of the entire area. It is difficult to reflect the distribution of surface cracks; (4) Gas concentration monitoring technology: Gas concentration monitoring takes a certain amount of time to reflect changes in coalbed methane, which may miss the early stages of leakage and cannot provide the precise location of cracks. The above-mentioned crack detection methods are mostly manual inspections, tunnel monitoring, etc., which are inefficient and have safety hazards.

[0044] See also Figure 1-3 A method for identifying surface gas-conducting fractures in shallow coal seams based on infrared thermal imaging comprises the following steps:

[0045] Step 1: Data collection: (1) including equipment preparation and site preparation. After the preparation is completed, the surface of the shallow coal seam is thermally imaged using an infrared thermal imager to collect thermal imaging data; (2) During the collection process, the target area is fully scanned according to a predetermined grid or strip path to obtain a high-resolution infrared thermal image sequence, and the location information and corresponding time information of each collection point are recorded;

[0046] Step 2: Image preprocessing: Use data processing software to remove noise from the collected thermal imaging images;

[0047] Step 3: Temperature feature extraction: Analyze the pre-processed infrared thermal image, and extract the temperature characteristics of the cracks based on the difference in temperature characteristics between the surface gas-conducting cracks and the surrounding medium in the shallow coal seam. In summer, the wind flow is relatively active, the solar radiation is strong, the atmospheric temperature is high, and the wind flow carries a lot of heat. The underground gas-conducting cracks are at a certain depth underground. Although there is wind flow passing through, the stratum has a certain barrier and buffering effect on the heat, and the underground wind flow is relatively less affected by the high temperature of the ground. The temperature of the underground gas-conducting cracks is relatively low, so the temperature of the gas-conducting cracks in the infrared thermal imaging image is lower than that of the surface. In winter, the surface gas-conducting cracks above the well are affected by the low-temperature cold air, and the temperature will drop rapidly. However, the underground gas-conducting cracks are located underground and are affected by the insulation effect of the stratum. The temperature of the underground wind flow is relatively high, and the temperature of the underground gas-conducting cracks is higher than that of the surface. Therefore, the temperature of the gas-conducting cracks in the infrared thermal imaging image is higher than that of the surface. By setting an appropriate temperature threshold range, the pixels that meet the temperature threshold in the image are extracted as the pixel set of the potential crack area, and the temperature anomaly area is screened out;

[0048] Step 4: Crack morphology identification: By using the Canny edge detection algorithm plug-in in ImageJ to perform edge detection on the processed thermal imaging image and adaptively adjusting the high and low thresholds, the edges of the cracks can be detected more accurately, the occurrence of false edges can be reduced, and the detected edges can be connected and fitted to obtain the geometric shape and direction information of the cracks, including the length, width, direction and branching of the cracks, and the possible contours of the gas-conducting cracks can be identified;

[0049] Step 5: Crack location and annotation: Combined with the location information recorded during the acquisition process, the identified cracks are located and annotated on the Geographic Information System (GIS) platform to visually display the distribution of cracks on the surface. At the same time, different types of cracks are classified and annotated based on their temperature and morphological characteristics.

[0050] Step 6: Result verification: Select some representative crack areas, measure the actual parameters of the cracks manually on site, compare and analyze them with the infrared thermal imaging recognition results, and then record and report the data.

[0051] The present invention utilizes the non-contact, rapid, and light-free characteristics of infrared thermal imaging technology to efficiently obtain surface temperature information of large areas of shallow coal seams, greatly improving the efficiency of crack identification and reducing manual labor intensity and the danger of on-site exploration. By precisely preprocessing and extracting features from infrared thermal images, combined with an improved edge detection algorithm, the morphology and position of gas-conducting cracks can be accurately identified, effectively overcoming the problems of traditional methods that are greatly interfered with by environmental factors and have low accuracy. This provides reliable technical support for gas prevention and control and surface ecological environment protection during shallow coal seam mining. This method locates and classifies cracks on a GIS platform, facilitating the management, analysis, and visualization of crack information, helping researchers and engineering technicians to intuitively understand crack distribution and formulate more scientific and reasonable mining plans and disaster prevention measures.

[0052] Among them, thermal imaging data collection requires choosing a suitable collection time on sunny days and without strong winds to ensure that the surface temperature field distribution is relatively stable and not severely affected by external factors such as solar radiation.

[0053] Among them; the infrared thermal imager needs to be parallel to the ground and maintain a suitable shooting distance (1.5-3 meters) to achieve the best imaging effect.

[0054] Among them; equipment preparation includes:

[0055] (1) Select a suitable drone that can carry infrared thermal imaging equipment;

[0056] (2) Select a suitable infrared thermal imaging instrument, ensuring that it has good resolution and sensitivity within the required temperature range and is equipped with a GPS positioning system;

[0057] (3) Configure a laptop or mobile terminal and install data processing software (Photoshop, ImageJ) for thermal imaging image analysis.

[0058] Among them, on-site preparation includes:

[0059] (1) Determine the monitoring area and set the UAV flight range;

[0060] (2) Understand the coal seam depth, fracture distribution and gas release characteristics in the monitoring area;

[0061] (3) Conduct a site survey to assess the terrain, vegetation, and meteorological conditions in the area, including temperature, humidity, and wind speed.

[0062] Among them, temperature feature extraction sets a suitable temperature threshold range, extracts the pixels that meet the temperature threshold in the image, and uses it as a pixel set of potential crack areas to screen out temperature anomaly areas. The threshold determination formula based on statistical analysis is shown as follows: By collecting infrared thermal imaging data of the normal surface area of ​​the shallow buried coal seam, a series of temperature samples T1, T2, ..., T n , first calculate the mean of these samples and standard deviation σ,

[0063]

[0064]

[0065] Set the lower limit of temperature abnormality threshold T min and upper limit T max for:

[0066]

[0067] Among them, k is the empirical coefficient, which effectively distinguishes normal and abnormal temperature areas through statistical laws.

[0068] Among them; data recording and reporting include:

[0069] (1) Record the specific location and characteristics of the cracks and save the thermal imaging images and analysis results;

[0070] (2) Prepare detailed monitoring reports, including potential risk assessments and remediation recommendations, such as regular monitoring measures, ventilation improvements, and safe evacuation route planning.

[0071] Among them; the denoising process first performs grayscale processing to convert the color infrared thermal image into a grayscale image to simplify the subsequent processing process and highlight the temperature information. Then, use the Gaussian filter algorithm in Photoshop to remove noise points in the image, improve the image clarity and remove image noise interference. Convert the image to Lab color mode, select the "a" or "b" channel in the channel panel, and apply the "Gaussian Blur" filter. By adjusting the blur radius, the noise can be effectively reduced. After performing the same operation on the "lightness" channel, convert the image back to RGB mode. You can also copy the layer to be processed, apply the "Gaussian Blur" filter to the layer, and then set the blending mode of the blurred layer to "Overlay". Use a black brush to remove the parts that do not need to be superimposed. Next, perform contrast enhancement on the image. By stretching the grayscale value range, the temperature difference in the image is made more obvious, which facilitates the extraction of crack features.

[0072] Purpose of the present invention: To improve detection efficiency: The present invention uses a highly sensitive infrared thermal imager to quickly collect thermal imaging data of the entire monitoring area during flight or ground inspection. Compared with manual inspections, it has a high degree of automation, can cover a large area in a short time, and promptly identify potential cracks. Real-time monitoring and rapid response: Through continuous monitoring, temperature changes can be captured in real time. Once a temperature abnormality occurs, it can quickly indicate potential gas-conducting cracks and take timely measures. Accurately locate cracks: Combined with GPS software, the identified crack locations can be accurately marked on a map, thereby forming a visual crack distribution map, which will help with subsequent safety assessments and governance.

[0073] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0074] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for identifying surface gas-conducting cracks in shallow coal seams based on infrared thermal imaging, characterized in that: The following steps are involved: Step 1: Data collection: (1) including equipment preparation and site preparation. After the preparation is completed, the infrared thermal imager is used to collect thermal imaging data of the shallow coal seam surface; (2) During the acquisition process, the target area is fully scanned according to a predetermined grid or strip path to obtain a high-resolution infrared thermal image sequence, and the position information and corresponding time information of each acquisition point are recorded; Step 2: Image preprocessing: Use data processing software to remove noise from the collected thermal imaging images; Step 3: Temperature feature extraction: Analyze the preprocessed infrared thermal image and extract the temperature characteristics of the shallow coal seam surface gas-conducting cracks based on the temperature differences between the cracks and the surrounding medium. By setting an appropriate temperature threshold range, the pixels in the image that meet the temperature threshold are extracted as the pixel set of potential crack areas, thereby screening out temperature anomaly areas. Step 4: Crack morphology identification: By using the Canny edge detection algorithm plug-in in ImageJ to perform edge detection on the processed thermal imaging image and adaptively adjusting the high and low thresholds, the edges of the cracks can be detected more accurately, the occurrence of false edges can be reduced, and the detected edges can be connected and fitted to obtain the geometric shape and direction information of the cracks, including the length, width, direction and branching of the cracks, and the possible contours of the gas-conducting cracks can be identified; Step 5: Crack location and annotation: Combined with the location information recorded during the acquisition process, the identified cracks are located and annotated on the Geographic Information System (GIS) platform to visually display the distribution of cracks on the surface. At the same time, different types of cracks are classified and annotated based on their temperature and morphological characteristics. Step 6: Result verification: Select some representative crack areas, measure the actual parameters of the cracks manually on site, compare and analyze them with the infrared thermal imaging recognition results, and then record and report the data.

2. The method for identifying surface gas-conducting fractures in shallow coal seams based on infrared thermal imaging according to claim 1, characterized in that: The thermal imaging data collection needs to be done on sunny days and without strong winds, and at a suitable time to ensure that the surface temperature field distribution is relatively stable and is not severely affected by external factors such as solar radiation.

3. The method for identifying surface gas-conducting fractures in shallow coal seams based on infrared thermal imaging according to claim 1, characterized in that: The infrared thermal imager needs to be parallel to the ground and maintain a suitable shooting distance (1.5-3 meters) to achieve the best imaging effect.

4. The method for identifying surface gas-conducting fractures in shallow coal seams based on infrared thermal imaging according to claim 1, characterized in that: The equipment preparation includes: (1) Select a suitable drone that can carry infrared thermal imaging equipment; (2) Select a suitable infrared thermal imaging instrument, ensuring that it has good resolution and sensitivity within the required temperature range and is equipped with a GPS positioning system; (3) Configure a laptop or mobile terminal and install data processing software (Photoshop, ImageJ) for thermal imaging image analysis.

5. The method for identifying surface gas-conducting fractures in shallow coal seams based on infrared thermal imaging according to claim 1, characterized in that: The site preparation includes: (1) Determine the monitoring area and set the UAV flight range; (2) Understand the coal seam depth, fracture distribution and gas release characteristics in the monitoring area; (3) Conduct a site survey to assess the terrain, vegetation, and meteorological conditions in the area, including temperature, humidity, and wind speed.

6. The method for identifying surface gas-conducting fractures in shallow coal seams based on infrared thermal imaging according to claim 1, characterized in that: The temperature feature extraction sets a suitable temperature threshold range, extracts the pixels that meet the temperature threshold in the image, and uses it as a pixel set of potential crack areas to screen out temperature anomaly areas. The threshold determination formula based on statistical analysis is shown as follows: By collecting infrared thermal imaging data of the normal surface area of ​​the shallow buried coal seam, a series of temperature samples T1 are obtained. T2,…,T n , first calculate the mean T and standard deviation σ of these samples, Set the lower limit of temperature abnormality threshold T min and upper limit T max for: Among them, k is the empirical coefficient, which effectively distinguishes normal and abnormal temperature areas through statistical laws.

7. The method for identifying surface gas-conducting fractures in shallow coal seams based on infrared thermal imaging according to claim 1, characterized in that: The data recording and reporting includes: (1) Record the specific location and characteristics of the cracks and save the thermal imaging images and analysis results; (2) Prepare detailed monitoring reports, including potential risk assessments and remediation recommendations, such as regular monitoring measures, ventilation improvements, and safe evacuation route planning.

8. The method for identifying surface gas-conducting fractures in shallow coal seams based on infrared thermal imaging according to claim 1, characterized in that: The denoising process begins with grayscaling, converting the color infrared thermal image to a grayscale image to simplify subsequent processing and highlight temperature information. Next, a Gaussian filter algorithm is used in Photoshop to remove noise points, improve image clarity, and eliminate image noise interference. The image is converted to Lab color mode. In the Channels panel, select the "a" or "b" channel and apply the "Gaussian Blur" filter. Adjusting the blur radius effectively reduces noise. The same operation is repeated for the "Luminance" channel before converting the image back to RGB mode. Alternatively, duplicate the layer to be processed, apply the "Gaussian Blur" filter to it, set the blurred layer's blending mode to "Overlay," and use a black brush to remove unwanted areas. Next, contrast enhancement is performed on the image to stretch the grayscale value range to make temperature differences more pronounced, facilitating crack feature extraction.

Images