An air bubble detection device and air bubble detection method based on light refraction and reflection

Abstract

The present invention discloses a bubble detection device and a bubble detection method based on light refraction and reflection, which include a light source module, a macro camera, and an image processing and analysis unit; the light source module is used to irradiate the infusion pipeline. Due to the change in the density of the propagation medium, there are phenomena of light refraction, reflection, focusing, and scattering, so that sharp and bright boundaries are presented on the pipe wall and the bubble edges; the macro camera is used to receive software instructions to perform close-range shooting of the infusion pipeline and transmit the image data to the data analysis and processing unit; the image processing and analysis unit analyzes and processes the pipeline image through technical means such as decolorization, sharpening, and edge detection, marks the bubble parts, counts the number of bubbles, calculates the bubble size, and issues an alarm according to the set threshold. The present invention uses light refraction and reflection for bubble detection, with high detection accuracy, simple structure, low cost, stability and reliability. It can not only detect bubbles, but also detect tiny droplets attached to the pipe wall.

Description

Technical Field

[0001] The present invention relates to a method and device for bubble detection, and particularly to a bubble detection device and method based on light refraction and reflection. Background Art

[0002] During the transportation of medical liquids, air in the infusion pipeline poses a significant threat to the life safety of patients. If a large number of bubbles enter the blood vessels, it may form an air embolism and cause serious complications, resulting in symptoms such as palpitation, chest tightness, increased heart rate, precordial pain, and decreased blood pressure in patients. In many clinical applications, a device that can reliably detect bubbles in the infusion pipeline is needed to promptly issue an alarm for bubbles or bubble groups in the pipeline and perform rapid disposal.

[0003] Currently, there are mainly three common methods for detecting bubbles in infusion pipelines: capacitance method, optoelectronic method, and ultrasonic detection method.

[0004] Capacitance method: A capacitance plate is placed on each side of the infusion tube to detect the change in capacitance between the two plates. Based on this change, the change in the internal medium is inferred to achieve the detection purpose. Advantages and disadvantages: Simple structure, easy to achieve non-contact measurement, but unstable performance, extremely vulnerable to circuit interference, and it is very difficult to eliminate this interference.

[0005] Optoelectronic method: Utilize the photoelectric effect of optoelectronic devices. Commonly used optoelectronic devices include photosensitive triodes, photosensitive diodes, etc. According to the volt-ampere characteristics, the relationship between the output voltage and the light intensity can be obtained. When there are bubbles in the blood delivery tube, due to the reflection of light and the different absorption of light by different media, the light intensity received by the photosensitive device changes, thereby causing a change in the output voltage. The optoelectronic type has advantages such as fast response and non-contact, and is widely used in automation monitoring and control technologies, but it is sensitive to the color of the medium and the difference between the liquid column and the bubble is not obvious.

[0006] Ultrasonic method: Ultrasonic waves propagate linearly in a uniform medium, but when they reach the interface or different media, they will undergo reflection and refraction, and obey the reflection and refraction laws similar to geometric optics. In detection technologies, commonly used methods include transmission method, reflection method, frequency method, etc. The ultrasonic detector has very little attenuation and strong penetration ability in the liquid column and solid, has obvious interface reflection and refraction, and at the same time has the high-frequency characteristics of ultrasonic waves, which is convenient for counting actual pulses and ultrasonic pulses to judge the size of the bubbles and the length of the continuous liquid column. The ultrasonic air detector has high sensitivity and good reliability, and can detect continuous bubbles in small gaps, but the hardware cost is high, the algorithm is complex, and high computing power support is required.

[0007] Restricted by the detection principle and method, the common defect of the above three detection methods is that the detection accuracy and sensitivity for tiny air bubbles in small-diameter infusion tubes are relatively low, the detection effect is not obvious, and visual observation is not possible, which greatly restricts the clinical application of the above devices. Summary of the Invention

[0008] The technical problem to be solved by the present invention is a bubble detection device and a bubble detection method based on light refraction and reflection. By utilizing the refraction and reflection phenomena that occur when light propagates in different media, optical imaging of the infusion tube and air bubbles is performed, and software algorithms are used to identify and analyze the image data, so as to detect air bubbles (groups) in the tube and issue an alarm in a timely manner. It breaks through the problems of air bubble detection in small-diameter tubes and extremely tiny air bubble detection internationally, and can effectively detect ultra-tiny air bubbles in an infusion tube with an inner diameter less than 1 mm while maintaining the detection accuracy and sensitivity without decline.

[0009] The present invention is realized through the following technical solutions: A bubble detection device based on light refraction and reflection includes a light source module, a macro camera, and an image processing and analysis unit;

[0010] Among them, the light source module is used to irradiate the infusion tube. When light propagates among the tube, the liquid medicine, and the air bubbles, due to the change in the density of the propagation medium, refraction, reflection, focusing, and scattering phenomena of light occur, so that sharp and bright boundaries appear on the tube wall and the edges of the air bubbles;

[0011] The macro camera, the signal output end of the macro camera is connected to the image processing and analysis unit. It uses a macro camera with an automatic focusing function to receive software instructions to perform close-range shooting of the infusion tube and transmit the image data to the data analysis and processing unit;

[0012] The image processing and analysis unit analyzes and processes the tube image through technical means such as decolorization, sharpening, and edge detection, marks the bubble parts, counts the number of air bubbles, calculates the size of the air bubbles, and issues an alarm according to the set threshold.

[0013] A bubble detection method based on light refraction and reflection of the present invention uses the light emitted by the light source module to irradiate the outer wall of the infusion tube and penetrate into the liquid medicine. Due to the different medium properties of air and the tube wall, the tube wall and the liquid medicine, and the liquid medicine and the air bubbles, there is a large density difference, and light undergoes multiple refractions and reflections when propagating in them;

[0014] The greater the density difference between the media, the greater the refraction angle of light when crossing the medium boundary. At a specific angle, there is an obvious brightness difference between the light in the two media, thus forming a bright distinguishable boundary;

[0015] The specific steps are as follows:

[0016] After the real-time image captured by the macro camera is transmitted to the back-end image processing and analysis unit, the image processing unit first performs color removal on the image. The algorithm is to scan the image and perform grayscale processing on the RGB values of each pixel point in the image;

[0017] The decolorized image needs to be further sharpened to improve the contrast of the image and enhance the accuracy of image recognition;

[0018] After the image is decolorized and sharpened, an edge detection algorithm is used to further process and recognize the image. The purpose of edge processing is to find the boundaries between the gas, liquid, and solid phases and improve the accuracy of bubble identification;

[0019] After edge detection is completed, for the graphic features of large bubbles and small bubbles, two different methods are used to detect the bubbles in the pipeline. The large bubble detection method uses the radial color gamut method for recognition, and the small bubbles are judged using the pattern matching method;

[0020] After identifying the bubbles existing in the pipeline, the image processing and analysis unit can issue an alarm in a timely manner and perform automatic disposal according to user settings, greatly reducing the risks existing in the process of liquid medicine infusion and reducing the intensity of manual detection and misjudgment behaviors.

[0021] As a preferred technical solution, in S1, there are two methods to convert a color image into a grayscale image:

[0022] The first method is to make the values of the three RGB components equal, and a grayscale image can be obtained after output;

[0023] The second method is to convert RGB to the YCbCr format and extract the Y component. The Y component in the YCbCr format represents the brightness of the image. Therefore, only the Y component is output, and the resulting image is a grayscale image. The pixels are processed using the RGB888 to YCrCb calculation formula:

[0024] Y = 0.299R + 0.587G + 0.114B

[0025] Cb = 0.568(B - Y) + 128 = -0.172R - 0.339G + 0.511B + 128

[0026] Cr = 0.713(R - Y) + 128 = 0.511R - 0.428G - 0.083B + 128.

[0027] As a preferred technical solution, in S2, the sharpening algorithm processes the image pixels according to the Laplace enhancement formula, y(m, n) = x(m, n) + λ * z(m, n), where x(m, n) is the image before processing, y(m, n) is the sharpened image, z(m, n) represents the edges and details (high-frequency part) of the enhanced image, and λ is the enhancement factor.

[0028] As a preferred technical solution, in S3, the edge detection algorithms include the Robert operator, Sobel operator, Laplace operator, lower right edge extraction algorithm, prewitt operator, Robinson operator, Kirsch operator, and Smoothed operator.

[0029] As a preferred technical solution, in S4, the radial color gamut method intercepts a small section of the pipeline image in the vertical pipeline direction for color gamut analysis, and judges whether there are bubbles according to the color component distribution and continuity in the chromatogram. By vertically scanning the pixels in the selected interval, when the continuity of the brightness of the scanning line is interrupted, it can be judged that the liquid flow is interrupted, and the bubble volume = the width of the continuous scanning flow interruption * the cross-sectional area of the pipeline;

[0030] The pattern matching method for microbubble recognition: Use a variable-sized window to scan the image in a rectangular area, and distinguish the content in the window by light and dark brightness to judge whether there is an arc composed of white pixels in the rectangular area. If it exists, it is determined as a bubble and marked. Let the side length of the rectangle be L, then the bubble volume size is calculated using V sphere = 4 / 3πr^3, where r = L / 2.

[0031] As a preferred technical solution, in S4, the specific steps of the pattern matching method for microbubble recognition are as follows:

[0032] Step 1: With the center of the rectangle as the center, the inscribed circle of the rectangular area is denoted as P1, and the radius is R1;

[0033] Step 2: Establish a second circle with the center of the rectangle as the center and a radius of R2, denoted as P2, where R1 > R2, and the difference is about 3 - 4 pixels;

[0034] Step 3: Calculate the proportion of bright and dark pixels in the area enclosed by P1 and P2. When the proportion of bright area pixels is greater than 30%, it is judged that there are bubbles in this area.

[0035] Compared with the traditional detection methods, the present invention has the following advantages:

[0036] 1. High detection accuracy. In the present invention, it can effectively detect microbubbles and bubble groups in the infusion pipeline or microbubbles adhering to the inner wall of the infusion pipeline. The detection accuracy can reach 1 uL or even higher, which is better than the photoelectric method, capacitance method, and ultrasonic detection method;

[0037] 2. Simple structure: In the present invention, for the structures related to the pipeline, only a macro camera is needed to image the pipeline, and the data after imaging is calculated and analyzed by a computer, so the structure is simple.

[0038] 3. Low cost: The components required in the present invention include a light source, a macro camera, and an embedded computer, all of which are mature components, with a short R & D cycle and a simple software algorithm.

[0039] 4. Stable and reliable: The method used in the present invention is less affected by external interference factors and has a low demand for computing power.

[0040] 5. It can not only detect bubbles but also detect tiny droplets adhering to the pipe wall. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0042] Figure 1 It is the schematic diagram of the bubble detection device based on light refraction and reflection of the present invention.

[0043] Figure 2 It is the schematic diagram of the light path formed in the pipeline after multiple reflections and refractions of the incident light of the present invention.

[0044] Figure 3 It is the schematic diagram of the sharp boundary formed by reflection and refraction in the pipeline.

[0045] Figure 4 It is the first boundary pattern formed by ultra - tiny bubbles (cluster).

[0046] Figure 5 It is the second boundary pattern formed by ultra - tiny bubbles (cluster).

[0047] Figure 6 It is the comparison diagram of the effects after image decolorization processing and sharpening.

[0048] Figure 7 It is the edge detection effect Figure 1 ;

[0049] Figure 8 It is the edge detection effect Figure 2 ;

[0050] Figure 9 It is the effect diagram of identifying large bubbles by the radial color gamut method.

[0051] Figure 10 Effect diagram of identifying microbubbles by the pattern matching scanning method. Specific implementation manners

[0052] All features disclosed in this specification, or steps in all methods or processes disclosed, except for mutually exclusive features and / or steps, can be combined in any manner.

[0053] Any feature disclosed in this specification (including any additional claims, abstract, and drawings), unless specifically described, can be replaced by other equivalent or similar-purpose alternative features. That is, unless specifically described, each feature is only an example of a series of equivalent or similar features.

[0054] In the description of the present invention, it should be understood that the orientation or positional relationships indicated by terms such as "one end", "the other end", "outer side", "upper", "inner side", "horizontal", "coaxial", "center", "end portion", "length", "outer end", etc. are based on the orientation or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation to the present invention.

[0055] In addition, in the description of the present invention, the meaning of "a plurality of" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0056] Spatial relative position terms used in the present invention, such as "upper", "above", "lower", "below", etc., are for the purpose of facilitating the description of the relationship between one unit or feature and another unit or feature as shown in the drawings. The spatial relative position terms may be intended to include different orientations of the device during use or operation other than the orientation shown in the drawings. For example, if the device in the drawing is flipped, the unit described as being "below" or "beneath" other units or features will be "above" other units or features. Therefore, the exemplary term "below" can encompass both the upper and lower orientations. The device can be oriented in other ways (rotated 90 degrees or other orientations), and the spatially related descriptive terms used herein can be interpreted accordingly.

[0057] In the present invention, unless otherwise clearly defined and limited, terms such as "arranged", "socketed", "connected", "penetrated", "plugged in" shall be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or integrated; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two components or the interaction relationship between two components, unless otherwise clearly defined. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0058] As Figure 1 shown, a bubble detection device based on light refraction and reflection of the present invention includes a light source module 1, a macro camera 2, and an image processing and analysis unit 3;

[0059] Among them, the light source module is used to irradiate the infusion pipeline. When light propagates between the pipeline, the liquid medicine, and the bubbles, due to the change in the density of the propagation medium, there are refraction, reflection, focusing, and scattering phenomena of light, so that sharp and bright boundaries appear on the pipe wall and the bubble edges;

[0060] The macro camera, the signal output end of the macro camera is connected to the image processing and analysis unit. It uses a macro camera with an automatic focusing function to receive software instructions to perform a close-up shot of the infusion pipeline and transmit the image data to the data analysis and processing unit;

[0061] The image processing and analysis unit analyzes and processes the pipeline image through technical means such as decolorization, sharpening, and edge detection, marks the bubble parts, counts the number of bubbles, calculates the bubble size, and issues an alarm according to the set threshold.

[0062] When the device works, the light emitted by the light source irradiates the outer wall of the infusion pipeline and penetrates into the liquid medicine. Since the medium properties of air and the pipe wall, the pipe wall and the liquid medicine, and the liquid medicine and the bubbles are different and have a large density difference, there are multiple refractions and reflections when light propagates in them, as Figure 2 shown.

[0063] The greater the density difference between the media, the greater the refraction angle of light when crossing the medium boundary. At a specific angle, there is an obvious brightness difference between the light in the two media, thus forming a bright distinguishable boundary, as Figure 3 shown.

[0064] For finer microbubbles suspended in the liquid or attached to the pipe wall, due to the surface tension effect, the surface curvature of the bubble is very large, thus generating a light focusing or scattering effect similar to that of a convex lens or a concave lens. Under the irradiation of light, there is an obvious brightness difference from the surrounding environment, thus providing feasibility for optical recognition, as Figure 4 and Figure 5 shown.

[0065] After the real-time image captured by the macro camera is transmitted to the back-end image processing and analysis unit, the image processing unit first performs color removal on the image. Its algorithm is to scan the image and perform grayscale processing on the RGB values of each pixel point in the image. There are two methods to convert a color image into a grayscale image:

[0066] The first method is to make the values of the three RGB components equal. After output, a grayscale image can be obtained.

[0067] The second method is to convert RGB to the YCbCr format and extract the Y component. The Y component in the YCbCr format represents the brightness of the image. Therefore, only the Y component is output, and the resulting image is a grayscale image.

[0068] In this embodiment, the pixel is processed using the RGB888 to YCrCb calculation formula:

[0069] Y = 0.299R + 0.587G + 0.114B;

[0070] Cb = 0.568(B - Y) + 128 = -0.172R - 0.339G + 0.511B + 128;

[0071] Cr = 0.713(R - Y) + 128 = 0.511R - 0.428G - 0.083B + 128.

[0072] The color-removed picture needs to be further sharpened. The purpose is to improve the contrast of the picture and enhance the accuracy of image recognition. The sharpening algorithm processes the picture pixels according to the Laplace enhancement formula.

[0073] y(m, n) = x(m, n) + λ * z(m, n), where x(m, n) is the picture before processing, y(m, n) is after sharpening, z(m, n) represents the edges and details (high-frequency part) of the enhanced image, and λ is the enhancement factor, as Figure 6 shown.

[0074] After the image is color-removed and sharpened, the edge detection algorithm is used to further process and identify the image. The purpose of edge processing is to find the boundaries between the gas, liquid, and solid phases and improve the accuracy of bubble identification.

[0075] Currently, mature edge detection algorithms with good detection effects include the Robert operator, Sobel operator, Laplace operator, lower right edge extraction algorithm, prewitt operator, Robinson operator, Kirsch operator, and Smoothed operator. In this embodiment, the Smoothed operator is used to identify the image, as Figure 7 andFigure 8 As shown

[0076] After the edge detection is completed, for the graphic features of large bubbles and micro-bubbles, the present invention uses two different methods to detect the bubbles in the pipeline.

[0077] The large bubble detection method uses the radial color gamut method for identification, and the micro-bubbles are judged using the pattern matching method.

[0078] The radial color gamut method is to intercept a small section of the pipeline image in the direction perpendicular to the pipeline for color gamut analysis, and judge whether there are bubbles according to the distribution and continuity of the color components in the chromatogram. As Figure 9 shown, by vertically scanning the pixels in the selected interval, when the continuity of the brightness of the scanning line is interrupted, it can be judged that the liquid flow is interrupted, and the bubble volume = the width of the continuous scanning interruption * the cross-sectional area of the pipeline.

[0079] Different from the large bubble recognition and judgment method, the micro-bubble recognition mainly uses the pattern matching method for recognition. As Figure 8 shown, use a variable-sized window to scan the image in a rectangular area, and distinguish the content in the window through light and dark brightness to judge whether there is an arc composed of white pixels in the rectangular area. If it exists, it is determined as a bubble and marked. Let the side length of the rectangle be L, then the bubble volume size is calculated using V sphere = 4 / 3πr^3, where r = L / 2. The specific steps are as follows:

[0080] Step 1: With the center of the rectangle as the center of the circle, the inscribed circle of the rectangular area is denoted as P1, and the radius is R1;

[0081] Step 2: Establish a second circle with the center of the rectangle as the center of the circle and a radius of R2, denoted as P2, where R1 > R2, and the difference is about 3 - 4 pixels;

[0082] Step 3: Calculate the proportion of light and dark pixels in the area surrounded by P1 and P2. When the proportion of bright area pixels is greater than 30%, it is judged that there are bubbles in the area, as Figure 10 shown.

[0083] After identifying the bubbles existing in the pipeline, the image processing and analysis unit can issue an alarm in time and perform automatic disposal according to the user settings, greatly reducing the risks existing in the infusion process of the liquid medicine, reducing the intensity of manual detection and the misjudgment behavior.

[0084] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be thought of without creative work should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope defined by the claims.

Claims

1. A bubble detection method based on light refraction and reflection, characterized in that: It includes a light source module, a macro camera, and an image processing and analysis unit; Among them, the light source module is used to irradiate the infusion pipeline. When light propagates among the pipeline, the liquid medicine, and the bubbles, due to the change in the density of the propagation medium, there are refraction, reflection, focusing, and scattering phenomena of light, so that sharp and bright boundaries appear on the pipe wall and the edge of the bubbles; The macro camera, the signal output end of the macro camera is connected to the image processing and analysis unit. It uses a macro camera with an automatic focusing function to receive software instructions to achieve close-range shooting of the infusion pipeline and transmit the image data to the data analysis and processing unit; The image processing and analysis unit analyzes and processes the pipeline image through techniques such as decolorization, sharpening, and edge detection, marks the bubble parts, counts the number of bubbles, calculates the bubble size, and issues an alarm according to the set threshold; The light emitted by the light source module irradiates the outer wall of the infusion pipeline and penetrates into the liquid medicine. Due to the different medium properties of air and the pipe wall, the pipe wall and the liquid medicine, and the liquid medicine and the bubbles, there is a large density difference, and there are multiple refractions and reflections when light propagates in them; The greater the density difference between the media, the greater the refraction angle of light when crossing the medium boundary. At a specific angle, there is an obvious brightness difference between the light in the two media, thus forming a bright distinguishable boundary; The specific steps are as follows: S1. After the real-time image captured by the macro camera is transmitted to the backend image processing and analysis unit, the image processing unit first performs decolorization processing on the image. The algorithm is to scan the image and perform grayscale processing on the RGB values of each pixel point in the image; S2. The decolorized picture needs to be further sharpened to improve the contrast of the picture and enhance the image recognition accuracy; S3. After the image is decolorized and sharpened, use the edge detection algorithm to further process and identify the image. The purpose of edge processing is to find the boundaries between the gas, liquid, and solid phases and improve the accuracy of bubble identification; S4. After edge detection is completed, for the graphic features of large bubbles and small bubbles, two different methods are used to detect the bubbles in the pipeline. The large bubble detection method uses the radial color gamut method for identification, and the small bubbles use the pattern matching method for judgment. The radial color gamut method is to intercept a small section of the pipeline picture in the direction perpendicular to the pipeline for color gamut analysis. According to the distribution and continuity of the color components in the color spectrum, it is judged whether there are bubbles. By vertically scanning the pixels in the selected interval, when the continuity of the brightness of the scanning line is interrupted, it can be judged that the liquid flow is interrupted. The bubble volume = the width of the continuous scanning interruption * the cross-sectional area of the pipeline; S5. After identifying the bubbles existing in the pipeline, the image processing and analysis unit can issue an alarm in time and perform automatic disposal according to the user settings, greatly reducing the risks existing in the process of liquid medicine infusion and reducing the intensity of manual detection and misjudgment behaviors; 2. The bubble detection method based on light refraction and reflection according to claim 1, characterized in that: In S1, there are two methods to convert a color image into a grayscale image: The first method is to make the values of the three RGB components equal, and a grayscale image can be obtained after output; The second method is to convert RGB to the YCbCr format and extract the Y component. The Y component in the YCbCr format represents the brightness of the image. Therefore, only the Y component is output, and the resulting image is a grayscale image. The pixels are processed using the RGB888 to YCrCb calculation formula: Y = 0.299R + 0.587G + 0.114B Cb = 0.568(B - Y) + 128 = -0.172R - 0.339G + 0.511B + 128 Cr = 0.713(R - Y) + 128 = 0.511R - 0.428G - 0.083B + 128.

3. The bubble detection method based on light refraction and reflection according to claim 1, wherein: In S2, the sharpening algorithm processes the picture pixels according to the Laplacian enhancement formula, y(m,n)=x(m,n)+λ∗z(m,n), where x(m,n) is the picture before processing, y(m,n) is the sharpened one, z(m,n) represents the edges and high-frequency part details of the enhanced image, and λ is the enhancement factor.

4. The bubble detection method based on light refraction and reflection according to claim 1, characterized in that: In S3, the edge detection algorithms include the Robert operator, Sobel operator, Laplace operator, lower right edge extraction algorithm, prewitt operator, Robinson operator, Kirsch operator, and Smoothed operator.

5. The bubble detection method based on light refraction and reflection according to claim 1, characterized in that: In S4: The pattern matching method for microbubble recognition: Use a variable-sized window to scan the image in a rectangular area, and distinguish the content within the window by light and dark brightness to determine whether there is an arc composed of white pixels in the rectangular area. If so, it is determined as a bubble and marked. Let the side length of the rectangle be L, then the bubble volume size is calculated using V sphere = 4 / 3πr^3, where r = L / 2.

6. The bubble detection method based on light refraction and reflection according to claim 1, characterized in that: In S4, the specific steps of the pattern matching method for microbubble recognition are as follows: Step 1: Take the center of the rectangle as the center of the circle, and the inscribed circle of the rectangular area is denoted as P1 with a radius of R1; Step 2: Establish a second circle with the center of the rectangle as the center of the circle and a radius of R2, denoted as P2, where R1 > R2, and the difference is 3 - 4 pixels; Step 3: Calculate the proportion of bright and dark pixels in the area enclosed by P1 and P2. When the proportion of bright area pixels is greater than 30%, it is determined that there is a bubble in the area.

Images