Conductive particle detection method

Abstract

The invention discloses a conductive particle detection method, which comprises the following steps: Step 1, defining a detection area and parameters: firstly, setting a window for particle detection, the window being composed of rectangles with odd side lengths, and then defining a search area of a gradient image; and Step 2, particle detection: in a search area of the gradient image, taking the center of the window as a reference point, sliding the window pixel by pixel, detecting a pixel peak value in the search area, judging and determining the position of the particle, and recording and storing coordinate values of the particle. According to the method, the maximum value of the local area is detected in a sliding window mode, accurate positioning of the particle center point can be ensured, the processing speed is greatly improved, large-scale images can be processed in real time, and the method is suitable for industrial online detection and other scenes.

Description

Technical Field

[0001] The present invention relates to the technical field of conductive particle pressing detection, and in particular to a conductive particle detection method. Background Art

[0002] Most existing particle detection methods rely on classic image processing algorithms, such as the Sobel operator, Canny edge detection, and Hough transform. These methods mainly identify particles by detecting gradient changes or edge information in images, but are usually sensitive to noise and cannot cope with images with high-density particles or severe strip interference. In addition, traditional algorithms have low accuracy and efficiency when processing large-scale images or complex backgrounds.

[0003] The particle detection method in the prior art has the following defects: 1. Sensitive to noise: In images with more noise, the accuracy of particle recognition is poor, and false detection or missed detection is prone to occur.

[0004] 2. Difficulty in processing high-density particles: In images with densely distributed particles, existing algorithms find it difficult to accurately distinguish adjacent particles, resulting in reduced detection accuracy.

[0005] 3. Low computational efficiency: In large images or complex scenes, the existing algorithms have high computational complexity and slow processing speed, which makes it difficult to meet the needs of real-time detection. Summary of the invention

[0006] The purpose of the present invention is to provide a technical solution for a conductive particle detection method to address the deficiencies in the prior art. The conductive particle detection method has simple steps and detects the maximum value of a local area by adopting a sliding window method, thereby ensuring the precise positioning of the center point of the particle, greatly improving the processing speed, and being able to process large-scale images in real time, and is suitable for scenarios such as industrial online detection.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solutions: A conductive particle detection method, characterized by comprising the following steps: Step 1. Define the detection area and parameters: First, set the window for particle detection. The window is composed of a rectangle with an odd side length, and then define the search area of ​​the gradient image. Step 2, particle detection: In the search area of ​​the gradient image, take the center of the window as the reference point to ensure that the center position is unique, slide the window pixel by pixel, detect the pixel peak in the search area, judge and determine the position of the particle, and record and store the coordinate value of the particle.

[0008] The conductive particle detection method has simple steps and detects the maximum value of the local area by using a sliding window method, which can ensure the precise positioning of the particle center point, greatly improve the processing speed, and can process large-scale images in real time. It is suitable for scenarios such as industrial online detection.

[0009] The present invention has the following beneficial effects due to the adoption of the above technical solution: 1. The conductive particle detection method of the present invention has simple steps. By adopting a sliding window method to detect the maximum value of a local area, the precise positioning of the center point of the particle can be ensured, which greatly improves the processing speed and can process large-scale images in real time. It is suitable for scenes such as industrial online detection.

[0010] 2. The present invention adopts the local maximum matching technology to make particle positioning more accurate, and can effectively separate adjacent particles even when the particles are densely distributed.

[0011] 3. The present invention effectively improves the robustness of the algorithm by expanding the image boundary and adjusting the matching window size. It is particularly suitable for images with high-density particles and complex backgrounds. While maintaining high-precision particle detection, this solution significantly improves the processing speed and is suitable for application scenarios such as industrial inspection and microscopic imaging. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The present invention will be further described below in conjunction with the accompanying drawings: Figure 1 A flow chart of a conductive particle detection method of the present invention; Figure 2 This is a flowchart of image preprocessing in the present invention; Figure 3 It is a structural schematic diagram of the window sliding along the gradient image in the present invention; Figure 4 It is a structural schematic diagram of image boundary extension in the present invention; Figure 5 It is a schematic diagram of the detection results of the number of particles and the average position of multiple particle detection effects in the present invention; Figure 6 It is a schematic diagram of the detection results of the number of particles and the average position of the single particle detection effect in the present invention; Figure 7 This is a schematic diagram of the results when no detection is performed in the present invention; Figure 8 It is a schematic diagram of the results after detection in the present invention. DETAILED DESCRIPTION

[0013] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0014] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only embodiments of a part of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0015] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions.

[0016] like Figure 1 and Figure 2 As shown, a conductive particle detection method of the present invention comprises the following steps: First, perform image preprocessing, such as Figure 2 As shown, image preprocessing includes the following steps: a. Perform brightness normalization on the input original image and scale the overall pixel value of the original image to adjust the overall image brightness to an appropriate range to improve the accuracy of subsequent particle detection; b. Then, the adjusted original image is subjected to edge enhancement processing, and the gradient information of the adjusted original image is extracted using an edge detection operator to form a gradient image.

[0017] The edge detection operator is used to extract the gradient information of the adjusted original image and form a gradient image, which specifically includes calculating the brightness change rate of each pixel in the adjusted original image.

[0018] By using an improved edge detection operator to perform gradient detection on the input image and enhancing the image contrast through normalization processing, the anti-noise performance is improved. At the same time, the algorithm's robustness to noise is improved and false detection is reduced.

[0019] Second, the image boundary is expanded to improve the particle detection effect. By expanding the image boundary and adjusting the matching window size, the robustness of the algorithm is effectively improved, which is particularly suitable for images with high-density particles and complex backgrounds. While maintaining high-precision particle detection, this solution significantly improves the processing speed and is suitable for application scenarios such as industrial inspection and microscopic imaging.

[0020] like Figure 4As shown, the image boundary extension includes the following steps: adding an extended boundary around the gradient image so that the particle information at the edge of the gradient image is not missed in the subsequent detection process. The width of the extended boundary is equal to 1 / 2 of the window of particle detection, which can ensure that each detected window has sufficient pixel data inside the gradient image and is not affected by the edge. The fill pixel value of the extended boundary is 0 to ensure that there is no interference with the information of the original image.

[0021] 3. Redefine the detection area and parameters and particle detection, such as Figure 3 shown.

[0022] Step 1. Define the detection area and parameters: First, set the window for particle detection. The window is composed of a rectangle with an odd side length, and then define the search area of ​​the gradient image. The size of the search area is the size of the gradient image minus the size of the window to avoid edge effects.

[0023] Step 2, particle detection: In the search area of ​​the gradient image, take the center of the window as the reference point to ensure that the center position is unique, slide the window pixel by pixel, detect the pixel peak in the search area, judge and determine the position of the particle, and record and store the coordinate value of the particle.

[0024] The specific steps of judging and determining the position of particles include: comparing the pixel intensity value in the window with the preset threshold value, if the pixel intensity value of the center point of the window is higher than the threshold value and is the local maximum value in the window, then the position is determined to be the position of the particle. By adopting the local maximum matching technology, the particle positioning is more accurate, and even in the case of densely distributed particles, adjacent particles can still be effectively separated.

[0025] Fourth, after particle detection, particle data statistics are performed, the positions of all detected particles are recorded, and the coordinates of each newly detected particle are statistically updated. The particle coordinates include the minimum coordinate value, the maximum coordinate value, and the cumulative coordinate value.

[0026] Each particle coordinate includes x and y coordinates.

[0027] Minimum coordinate value: Compare all particle coordinates and get the minimum x and y coordinate values.

[0028] Maximum coordinate value: Compare all particle coordinates and get the maximum x and y coordinate values.

[0029] Accumulated coordinate values: Accumulate the x and y coordinates of all particles for subsequent calculation of the average position.

[0030] The calculation formula is as follows: Minimum x coordinate: min(x1, x2, ..., xn) Minimum y-coordinate: min(y1, y2, ..., yn) Maximum x-coordinate: max(x1, x2, ..., xn) Maximum y-coordinate: max(y1, y2, ..., yn) Cumulative x-coordinate: ∑xi Cumulative y-coordinate: ∑yi.

[0031] 5. After the particle data is counted, the number of particles and the average position are calculated. The total number of particles detected is counted. If no particles are detected, the number of particles is recorded as 0 and the average position is (0, 0). If particles are detected, the average position of the particles is calculated using the accumulated coordinate values.

[0032] Average x coordinate: Average_x = ∑xi / Total Particles Average y coordinate: Average_y = ∑yi / Total Particles.

[0033] Total Particles means: the total number of particles.

[0034] 6. Record the detection results of the number of particles and the average position into a structure or other data storage format. The detection results of the number of particles and the average position are as follows: Figures 5 to 8 shown.

[0035] The conductive particle detection method has simple steps and detects the maximum value of the local area by using a sliding window method, which can ensure the precise positioning of the particle center point, greatly improve the processing speed, and can process large-scale images in real time. It is suitable for scenarios such as industrial online detection.

[0036] The above are only specific embodiments of the present invention, but the technical features of the present invention are not limited thereto. Any simple changes, equivalent replacements or modifications made based on the present invention to achieve basically the same technical effects are all included in the protection scope of the present invention.

Claims

1. A conductive particle detection method, characterized in that The steps include: Step 1. Define the detection area and parameters: First, set the window for particle detection, which is composed of a rectangle with an odd side length, and then define the search area of ​​the gradient image; Step 2, particle detection: In the search area of ​​the gradient image, with the center of the window as the reference point, slide the window pixel by pixel, detect the pixel peak in the search area, judge and determine the position of the particle, and record and store the coordinate value of the particle.

2. A conductive particle detection method according to claim 1, characterized in that: The size of the search area in step 1 is the size of the gradient image minus the size of the window.

3. The conductive particle detection method according to claim 1, wherein: The determination of the particle position in Step 2 specifically includes the following steps: comparing the pixel intensity value in the window with a preset threshold value; if the pixel intensity value at the center point of the window is higher than the threshold value and is a local maximum value within the window, then the position is determined to be the particle position.

4. The conductive particle detection method according to claim 1, wherein: Image preprocessing and image boundary expansion are performed before the step of defining the detection area and parameters to improve the particle detection effect.

5. The conductive particle detection method according to claim 4, wherein: The image preprocessing comprises the following steps: a. Performing brightness normalization on the input original image and scaling the overall pixel value of the original image; b. Then, edge enhancement processing is performed on the adjusted original image, and gradient information of the adjusted original image is extracted using an edge detection operator to form the gradient image.

6. A conductive particle detection method according to claim 5, characterized in that: The extracting gradient information of the adjusted original image and forming a gradient image by using an edge detection operator specifically includes calculating the brightness change rate of each pixel in the adjusted original image.

7. The conductive particle detection method according to claim 5, characterized in that: The image boundary extension comprises the following steps: adding an extension boundary around the gradient image, wherein the width of the extension boundary is equal to 1 / 2 of the window for particle detection, and the filling pixel value of the extension boundary is 0.

8. The conductive particle detection method according to claim 1, wherein: After the particle detection, particle data statistics are performed, the positions of all detected particles are recorded, and the coordinates of each newly detected particle are statistically updated. The particle coordinates include a minimum coordinate value, a maximum coordinate value, and a cumulative coordinate value.

9. The conductive particle detection method according to claim 8, wherein: After the particle data is counted, the number of particles and the average position are calculated. The total number of detected particles is counted. If no particles are detected, the number of particles is recorded as 0 and the average position is (0, 0). If particles are detected, the average position of the particles is calculated using the accumulated coordinate values.

10. The conductive particle detection method according to claim 9, characterized in that: Record the detection results of particle number and average position into a structure or other data storage format.

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