Fiber end face cutting angle detection method, device, equipment and medium

Abstract

The application discloses a fiber end face cutting angle detection method, device, equipment and storage medium, and the method comprises the steps of obtaining a first camera and a second camera which are perpendicular to each other, collecting a first image of a fiber end face under a first visual angle and a second image of the fiber end face under a second visual angle respectively, processing the first image and the second image respectively, calculating a first visual inclination angle of the fiber end face under the first visual angle and a second visual inclination angle of the fiber end face under the second visual angle, and calculating a cutting angle of the fiber end face based on a preset mathematical model according to the first visual inclination angle and the second visual inclination angle. The accuracy and reliability of the cutting angle detection are improved by adopting the double cameras which are perpendicular to each other to perform multi-angle observation and combining the mathematical model of three-dimensional geometric characteristics to calculate the cutting angle.

Description

Technical Field

[0001] This application relates to the field of optical fiber fusion splicing technology, and in particular to a method, apparatus, device and storage medium for detecting the cutting angle of an optical fiber end face. Background Technology

[0002] In fiber optic communication networks, the quality of fiber optic splices has a decisive impact on system transmission performance and long-term reliability. Fusion splicing, as a core splicing technology, is widely used in long-distance trunk transmission, high-density cabling in data centers, and high-performance scenarios due to its low loss, high mechanical strength, and excellent stability. The preparation of the fiber end face is a critical step before fusion splicing, typically using a specialized cleaver to create a smooth, flat end face that is strictly perpendicular to the fiber axis. The perpendicularity deviation between the end face and the axis, i.e., the cleaving angle, is a core parameter for measuring cleaving quality, and its value directly affects the fusion loss level and splicing success rate. For standard single-mode fiber (G.652) and multimode fiber (G.651), industry standards generally require the cleaving angle to be no more than 1°. In precision optical systems or ultra-low-loss scenarios, this indicator needs to be further controlled within 0.5°.

[0003] Currently, to achieve accurate measurement of the cutting angle, a common approach is to use two mutually perpendicular camera systems to acquire image data of the fiber end face from orthogonal viewing angles. However, current mainstream detection methods have significant drawbacks: they only perform simple linear fitting on single-view images or directly derive the cutting angle from dual-view measurements using the root mean square method. Because they fail to fully consider the geometric characteristics of the fiber end face in three-dimensional space and lack accurate mathematical modeling of multi-view observation data, the calculated results deviate significantly from the actual cutting angle. This error is further exacerbated by the presence of minor scratches, contamination, or ambient light interference on the end face, making it difficult to meet the stringent requirements of high-precision fusion splicing for angle detection. Summary of the Invention

[0004] The purpose of this application is to provide a method, apparatus, device, and storage medium for detecting the cutting angle of an optical fiber end face, thereby solving the technical problem of inaccurate detection of the cutting angle of an optical fiber end face in the prior art.

[0005] In a first aspect, embodiments of this application provide a method for detecting the cutting angle of an optical fiber end face, including: A first and a second camera, which are perpendicular to each other, are used to acquire a first image of the fiber end face from a first viewpoint and a second image from a second viewpoint, respectively. The first image and the second image are processed respectively to calculate the first viewing tilt angle of the fiber end face under the first viewing angle and the second viewing tilt angle under the second viewing angle. The cutting angle of the fiber end face is calculated based on the first apparent tilt angle and the second apparent tilt angle, using a preset mathematical model.

[0006] Secondly, embodiments of this application provide a device for detecting the cutting angle of an optical fiber end face, comprising: The image acquisition module is used to acquire images from a first camera and a second camera that are perpendicular to each other, respectively capturing a first image of the fiber end face from a first perspective and a second image from a second perspective. An image processing module is used to process the first image and the second image respectively, and calculate the first viewing tilt angle of the fiber end face under the first viewing angle and the second viewing tilt angle under the second viewing angle; An angle calculation module is used to calculate the cutting angle of the fiber end face based on the first apparent tilt angle and the second apparent tilt angle, using a preset mathematical model.

[0007] Thirdly, embodiments of this application provide an electronic device, which includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the fiber optic end face cutting angle detection method described in any of the above claims.

[0008] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the fiber optic end face cutting angle detection method described in any of the preceding claims.

[0009] This application provides a method, apparatus, device, and storage medium for detecting the cutting angle of an optical fiber end face. The method includes: acquiring a first camera and a second camera that are perpendicular to each other to capture a first image of the optical fiber end face from a first viewing angle and a second image from a second viewing angle, respectively; processing the first image and the second image to calculate a first viewing tilt angle of the optical fiber end face from the first viewing angle and a second viewing tilt angle from the second viewing angle; and calculating the cutting angle of the optical fiber end face based on a preset mathematical model according to the first viewing tilt angle and the second viewing tilt angle. By using two perpendicular cameras for multi-view observation and combining a mathematical model of three-dimensional geometric characteristics for cutting angle calculation, the accuracy and reliability of cutting angle detection are improved. Attached Figure Description

[0010] Figure 1 This is a flowchart illustrating one step of the fiber end face cutting angle detection method provided in this application embodiment; Figure 2 This is a flowchart illustrating the steps for obtaining the first and second viewing tilt angles according to an embodiment of this application. Figure 3 This is another flowchart illustrating the steps for obtaining the first and second viewing tilt angles provided in the embodiments of this application; Figure 4 This is a schematic diagram of a cut optical fiber provided in an embodiment of this application; Figure 5 This is a schematic diagram of the projection of the cut optical fiber at different angles provided in the embodiments of this application; Figure 6 This is a schematic diagram of a cut optical fiber provided in an embodiment of this application; Figure 7 This is a schematic diagram of a rotation angle view of a cut optical fiber ribbon provided in an embodiment of this application; Figure 8 This is a schematic diagram of a first image acquired by a first camera according to an embodiment of this application; Figure 9 This is a schematic diagram of the left fiber optic image of a schematic diagram acquired by the first camera provided in an embodiment of this application; Figure 10 This is a schematic diagram of the right-side fiber optic image of a schematic diagram acquired by the first camera provided in an embodiment of this application; Figure 11 This is a schematic diagram of corner point annotation of a schematic diagram acquired by the first camera provided in an embodiment of this application; Figure 12 This is a schematic diagram of a fiber optic end face cutting angle detection device provided in an embodiment of this application; Figure 13 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application; Figure 14 This is another structural schematic diagram of the electronic device provided in the embodiments of this application. Detailed Implementation

[0011] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0012] It should be understood that the steps described in the method embodiments disclosed in this application may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this application is not limited in this respect.

[0013] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0014] In practical applications, the accuracy of the fiber end face cutting angle detection has a decisive impact on the splicing quality and system stability. In related technologies, after acquiring images of the fiber end face by mutually perpendicular cameras, the images acquired by the two cameras are fitted separately. Specifically, simple linear fitting or root mean square calculation of the two field-view angles is used. Compared with the true tilt angle (cutting angle), the error is large. The deviation mainly comes from the independent processing of the single-view image, which makes the cutting angle detection inaccurate.

[0015] To address the technical problems existing in related technologies, this application provides a method for detecting the cutting angle of an optical fiber end face. Please refer to [link to relevant documentation]. Figure 1 , Figure 1 This is a flowchart illustrating one step of the fiber end face cutting angle detection method provided in this application embodiment, which includes steps 101 to 103.

[0016] Step 101: Obtain a first camera and a second camera that are perpendicular to each other, and respectively capture a first image of the fiber end face from a first viewpoint and a second image from a second viewpoint.

[0017] Here, the first camera and the second camera refer to two independent imaging devices used to acquire images of the fiber optic end face. The first camera and the second camera are set to be perpendicular to each other to obtain two-dimensional projection information of the fiber optic end face from different viewing directions. The first viewpoint and the second viewpoint correspond to the directions in which the first camera and the second camera observe the fiber optic end face, respectively. Since the two cameras are perpendicular to each other, the first viewpoint and the second viewpoint are also perpendicular to each other, providing projection views of the fiber optic end face on two orthogonal planes.

[0018] In one embodiment, a first camera and a second camera, which are perpendicular to each other, are used to acquire images to obtain a first image of the fiber end face from a first viewpoint and a second image from a second viewpoint.

[0019] Step 102: Process the first image and the second image respectively to calculate the first viewing tilt angle of the fiber end face under the first viewing angle and the second viewing tilt angle under the second viewing angle.

[0020] The first and second apparent tilt angles represent the tilt angles of the fiber end face as seen in the first and second images, respectively. These apparent tilt angles are the representation of the actual three-dimensional cutting angle of the fiber end face on the two-dimensional projection plane, extracted from the respective images using image processing techniques.

[0021] In one embodiment, after acquiring and processing images through two mutually perpendicular cameras to obtain a first image and a second image, the first image and the second image are processed respectively to calculate the first viewing angle and the second viewing angle of the fiber end face under the first viewing angle and the second viewing angle under the second viewing angle.

[0022] For example, after acquiring the first and second images of the fiber end face, these images need to be processed to extract the apparent tilt angle of the fiber end face. Specifically, the processing can involve first converting the images to grayscale and enhancing their contrast, then identifying the edges of the fiber end face using an edge detection algorithm (such as the Canny or Sobel operator), and then using a simple least-squares method to fit a straight line to the detected edge points, thereby obtaining a straight line representing the edge of the fiber cut surface. The inclination of this straight line is considered the apparent tilt angle of the fiber end face at that viewpoint. This process is performed independently for the first and second images to obtain the first and second apparent tilt angles.

[0023] Furthermore, upon obtaining the first and second viewing tilt angles, the first and second images are processed respectively, such as... Figure 2 As shown, Figure 2 This is a flowchart illustrating the steps for obtaining a first viewing tilt angle and a second viewing tilt angle provided in an embodiment of this application, wherein the steps include steps 201 to 203.

[0024] Step 201: Preprocess the first image or the second image. The preprocessing includes denoising the first image or the second image based on median filtering and morphological operations. Step 202: Perform image segmentation processing on the preprocessed first image or second image to obtain the left fiber image and the right fiber image of the first image or second image; Step 203: Process the left and right fiber images to obtain the first viewing tilt angle of the fiber end face under the first viewing angle or the second viewing tilt angle under the second viewing angle.

[0025] In one embodiment, preprocessing the first or second image can improve image quality, providing clearer and more reliable input for subsequent image segmentation and feature extraction. During preprocessing, median filtering is used for image denoising. Median filtering is a non-linear digital filtering technique that replaces a pixel value with the median value within its neighborhood. This effectively removes discrete noise such as salt-and-pepper noise while preserving edge information to some extent, avoiding edge blurring that may occur with linear filtering. Besides median filtering, Gaussian filtering, bilateral filtering, or wavelet denoising can also be used for noise suppression. Morphological operations are used to denoise the image; these operations are a series of operations based on image shape, including erosion, dilation, opening, and closing operations. Through preprocessing, small-sized noise in the image can be effectively removed, image edges smoothed, broken lines connected, or adhered regions separated.

[0026] Image segmentation is performed on the preprocessed first or second image to accurately separate the fiber optic region from the background and further distinguish the left and right sides of the fiber. Image segmentation is a crucial step in image processing, dividing the image into multiple regions with similar features to facilitate independent analysis of specific target regions. Besides threshold segmentation, various segmentation algorithms can be employed, such as edge detection, region growing, and clustering segmentation. Segmenting the fiber optic image into left and right sides allows for separate processing of potential left-right asymmetry at the fiber end face, or it can improve the robustness and accuracy of tilt angle calculation by combining information from both sides.

[0027] When obtaining the left and right fiber optic images, the following can be included: The first or second image after preprocessing is binarized by applying a threshold, and the pixel statistics of the binarized image are obtained by performing pixel statistics in the horizontal direction; Based on the pixel statistics, the first image or the second image is segmented to obtain the left fiber image and the right fiber image corresponding to the first image or the second image.

[0028] Specifically, after preprocessing (including denoising) the fiber end-face image, to accurately identify the fiber region and segment it into left and right parts, the preprocessed image is first binarized using a threshold. This simplifies the complex grayscale image into black and white, significantly enhancing the contrast between the fiber region and the background region, thus effectively suppressing the interference of background noise and uneven illumination on subsequent segmentation. Subsequently, pixel statistics are performed on the binarized image in the horizontal direction, generating a pixel statistical result reflecting the horizontal distribution characteristics of the fiber. This statistical result can robustly reveal the overall outline and approximate location of the fiber because the pixel density in the fiber region is typically much higher than in the background region, forming a clear feature on the statistical curve. Based on this pixel statistical result, the left and right boundaries of the fiber can be accurately identified, and the original image can be segmented accordingly, resulting in independent and clear images of the left and right fiber regions.

[0029] In addition, when processing based on pixel statistics, the following are included: Based on pixel statistics, the discontinuities from the background region to the fiber optic region and from the fiber optic region to the background region are identified. The left and right boundaries of the optical fiber are determined based on the discontinuity points, and the first or second image is segmented according to the left and right boundaries to obtain the left and right optical fiber images corresponding to the first or second image.

[0030] In other words, based on the binarization of the preprocessed image and the acquisition of horizontal pixel statistics, the system further introduces the identification of "discontinuities" in these statistics. These discontinuities precisely indicate the locations of abrupt changes in pixel density from the background region to the fiber region and from the fiber region to the background region in the image. By accurately identifying these discontinuities, the system can reliably determine the left and right boundaries of the fiber in the image. Subsequently, these precisely determined boundaries are used to segment the image, thereby accurately extracting the fiber region and forming the left and right fiber images for subsequent analysis.

[0031] Pixel statistics refer to the data sequence obtained by counting pixels column by column in the horizontal direction of the binarized image. This sequence reflects the density distribution of pixels in each column of the image, typically showing higher pixel values ​​in fiber optic regions and lower pixel values ​​in background regions. Discontinuities are areas in the pixel statistics where the values ​​change significantly. These changes usually correspond to the boundaries of different regions in the image (such as the background and fiber optics). The left and right boundaries of the fiber optics refer to the start and end positions of the fiber optics in the horizontal direction of the image. One way to determine the boundaries is to scan from left to right after identifying multiple discontinuities, identifying the first discontinuity that transitions from a low pixel value region (background) to a high pixel value region (fiber optics) as the left boundary of the fiber optics, and the last discontinuity that transitions from a high pixel value region (fiber optics) to a low pixel value region (background) as the right boundary of the fiber optics. Another method is to combine the known size range or shape characteristics of the fiber optics to screen and verify the identified discontinuities, selecting the pair of discontinuities that best match the actual boundary characteristics of the fiber optics as the left and right boundaries of the fiber optics.

[0032] Based on the above processing method, the original acquired fiber optic end-face image is refined. First, median filtering and morphological operations are used to preprocess the image, resulting in higher-quality image data. Then, image segmentation is performed on the preprocessed image to accurately separate the fiber optic region from the complex background, further subdividing it into left and right fiber optic images. Finally, by processing these segmented left and right fiber optic images, the first apparent tilt angle of the fiber optic end-face under the first viewpoint and the second apparent tilt angle under the second viewpoint can be calculated more accurately and robustly.

[0033] Furthermore, when calculating the first apparent tilt angle of the fiber end face from the first viewing angle or the second apparent tilt angle from the second viewing angle based on the obtained left and right fiber images, a fitting process is performed by combining the left and right fiber images, such as... Figure 3 As shown, the steps to obtain the first or second apparent tilt angle include steps 301 to 303.

[0034] Step 301: Perform corner detection on the left fiber image and / or the right fiber image to generate a corner set containing multiple potential edge feature points; Step 302: Based on the corner point set, robust line fitting is performed using a random sampling consensus algorithm to obtain the best-fitting straight line at the edge of the fiber optic cut surface. Step 303: Based on the parameters of the best-fit straight line, determine the inclination of the fiber optic cut surface edge in the first or second image, and perform directional correction on the inclination to calculate the first apparent tilt angle of the fiber optic end face under the first viewing angle or the second apparent tilt angle under the second viewing angle.

[0035] Corner detection identifies pixels in an image that exhibit significant changes in direction or brightness. These pixels typically represent image features, such as edge intersections or local maxima of texture. Its role is to extract key feature points on the edges of fiber optic cut surfaces, providing a data foundation for subsequent line fitting. Corner detection can be implemented using various algorithms, such as Harris corner detection, Shi-Tomasi corner detection, or FAST corner detection. These algorithms determine corners by analyzing grayscale changes in the pixel's neighborhood. For example, Harris corner detection evaluates the corner response of pixels by calculating the image's autocorrelation matrix. The corner set is the collection of all identified corners after corner detection. The set can be a list or array containing corner coordinates (x, y), and each corner can also include its confidence or intensity information. Random Sample Consensus (RANSAC) is an iterative algorithm used to estimate the parameters of a mathematical model from a dataset containing outliers. It generates candidate models by randomly selecting subsets of data and evaluates how well these models fit the entire dataset to find the optimal model. Orientation correction adjusts the calculated tilt angle based on the relationship between the actual physical orientation and the image coordinate system, making it consistent with the actual physical tilt direction. This ensures that the calculated apparent tilt angle accurately reflects the true tilt direction of the fiber end face, avoiding orientation errors caused by differences between the image coordinate system and the physical coordinate system.

[0036] For example, based on the obtained images of the left and right optical fibers, corner detection is first performed on these images to extract pixels with significant features on the edge of the fiber optic cut surface, thereby generating a corner set containing multiple potential edge feature points. Given that the edge of the fiber optic cut surface may contain irregularities or noise points, directly fitting a straight line can easily introduce errors. Therefore, based on this corner set, a robust straight line fitting algorithm is used. Candidate straight line models are constructed by iteratively selecting a subset from the corner set and evaluating their fit with the remaining corner points. Finally, the model with the most interior points is selected as the best-fitting straight line for the fiber optic cut surface edge. Simultaneously, the tilt angle is corrected to ensure that the calculated first apparent tilt angle of the fiber end face in the first viewpoint or the second apparent tilt angle in the second viewpoint is consistent with the actual physical tilt direction.

[0037] In actual processing, corner points can be filtered before fitting. Specifically, this includes filtering corner points by removing those whose distance from the fiber optic cut surface area is greater than a first threshold and those whose confidence level is lower than a second threshold.

[0038] Specifically, before robust line fitting, each corner point in the corner point set is evaluated. First, the distance from each corner point to the fiber optic cut surface region is calculated, and corner points with distances greater than a preset first threshold are removed. These removed corner points are typically background noise in the image, non-cut surface features on the fiber itself, or detection errors, and are unrelated to the fiber optic cut surface edge. Second, the system also evaluates the confidence level of each corner point and removes those with confidence levels lower than a preset second threshold. These low-confidence corner points are often weak features or uncertain points identified by the corner detection algorithm. Through this dual filtering mechanism, outliers and low-quality points in the corner point set can be effectively eliminated, resulting in a cleaner and more representative subset of corner points. This optimized subset of corner points serves as input to the Random Sampling Consensus Algorithm (RANSAC), significantly improving the accuracy and convergence speed of the RANSAC algorithm when fitting the best-fit line to the fiber optic cut surface edge, ultimately making the calculation results of the first apparent tilt angle of the fiber end face in the first viewpoint or the second apparent tilt angle in the second viewpoint more accurate and reliable.

[0039] When performing line fitting based on a set of corner points, the following steps are included: Set the maximum number of iterations and the distance threshold; In each iteration, the first and second corner points are selected from the set of corner points to construct candidate line models; Calculate the distance from all corner points in the corner point set except for the first and second corner points to the candidate straight line model, and mark the corner points whose distance is less than the distance threshold as interior points; Record the number of interior points and proceed to the next iteration. After completing the maximum number of iterations, select the candidate line model with the most interior points as the best-fit line.

[0040] Specifically, by pre-setting a maximum number of iterations and a distance threshold, these two parameters jointly determine the RANSAC algorithm's operating strategy and the criteria for judging interior points. In each iteration of the algorithm, the system randomly selects two corner points from the detected set of corner points and uses these two points to construct a temporary candidate line model. Subsequently, the system calculates the distances from all other corner points in the set to this candidate line and, based on the preset distance threshold, marks corner points with distances less than the threshold as "interior points" supporting the model. This process is repeated until the preset maximum number of iterations is reached. After all iterations are completed, the system selects the candidate line model with the most interior points as the best-fit line for the fiber optic cut edge.

[0041] Based on the processing methods described and associated with step 202 above, noise interference such as burrs, scratches, or irregularities at the edge of the fiber optic cut surface can be effectively addressed, avoiding the drawbacks of traditional edge detection methods being susceptible to anomalies. Through robust straight-line fitting using corner detection and a random sampling consistency algorithm, the true geometric features of the fiber optic cut surface edge can be accurately extracted from noisy data, improving the calculation accuracy and robustness of the first apparent tilt angle of the fiber end face under a first viewing angle or the second apparent tilt angle under a second viewing angle. Because an accurate and reliable data foundation is provided, the angles obtained during the calculation of the cutting angle have better accuracy and stability.

[0042] Combining the above processing steps, after acquiring the first or second image of the fiber end face, the image is first preprocessed. For example, a 3x3 or 5x5 sliding window can be used to perform median filtering on the image to effectively remove salt-and-pepper noise. Subsequently, morphological opening operations (erosion followed by dilation) can be performed to eliminate small bright spot noise and break small connections, followed by morphological closing operations (dilation followed by erosion) to fill small holes and broken edges, thereby further smoothing the image and removing small impurities. After preprocessing, the image is segmented. For example, Otsu's method or adaptive thresholding can be applied to binarize the preprocessed image to separate the fiber region from the background. Next, the pixels of the binarized image are statistically analyzed in the horizontal direction. By analyzing abrupt changes in pixel values ​​(e.g., from 0 to 255 or from 255 to 0), the left and right boundaries of the fiber are identified. Based on these boundaries, the image is cropped or masked to obtain the left and right fiber images. Finally, the apparent tilt angle is calculated based on these segmented images. For example, Canny edge detection can be performed on the left and right fiber images respectively to extract the edges of the fiber cleavage surface. Then, Hough transform or Random Sample Consensus (RANSAC) algorithm is applied to the extracted edge point set to fit a straight line, obtaining the linear equation of the fiber cleavage surface edge. Based on the slope of the fitted line, the tilt angle of the fiber end face at the current viewing angle is calculated, and necessary coordinate system transformations and orientation corrections are performed to obtain the first or second viewing tilt angle.

[0043] Clearly, by combining the above image processing procedures, the accuracy and robustness of calculating the first and second viewing tilt angles from the image can be effectively improved, thus providing reliable data support for the final accurate calculation of the cutting angle of the fiber end face, and more effectively avoiding measurement errors caused by image quality issues.

[0044] Step 103: Calculate the cutting angle of the fiber end face based on the first tilt angle and the second tilt angle using a preset mathematical model.

[0045] In one embodiment, after obtaining the first tilt angle and the second tilt angle, the cutting angle of the fiber end face is calculated based on a preset mathematical model according to the first tilt angle and the second tilt angle.

[0046] Specifically, after obtaining the first and second apparent tilt angles of the fiber endface from two mutually perpendicular viewing angles, it is necessary to convert this two-dimensional projection information into the actual three-dimensional cutting angle of the fiber endface. This can be achieved by applying a pre-defined mathematical model. For example, this mathematical model could be a formula based on trigonometric functions, which takes the two apparent tilt angles as input and calculates the true tilt angle of the fiber endface relative to the fiber axis through geometric transformations. This model is established and calibrated during system design to ensure that it accurately reflects the correspondence between the two-dimensional projection and the actual three-dimensional angle. By substituting the calculated first and second apparent tilt angles into this model, the cutting angle of the fiber endface can be obtained.

[0047] In practical applications, when calculating the cutting angle based on the processing method described above, more accurate results can be obtained. The following is a corresponding explanation based on practical applications, such as... Figure 4 As shown, Figure 4 This is a schematic diagram of a cut optical fiber provided in an embodiment of this application, and for Figure 4 For the optical fiber shown, the projection shape at different angles can be as follows: Figure 5 As shown, when calculating angles, two mutually perpendicular cameras always display a certain rotation angle. and The projected image below.

[0048] For ease of calculation, let the diameter of the optical fiber be... The major axis of the sectional ellipse is As attached Figure 6 As shown. The actual cutting angle is... The formula is: ; When the first camera (camera X) rotates at an angle of... When taking a photo from this angle, its viewing angle is... satisfy: ; in See attached document Figure 7 As shown, by combining the above formulas, we obtain: ; Similarly, the viewing angle of the second camera (camera Y) at this time... satisfy: ; Further simplification using the trigonometric induction formula yields: ; Therefore, by integrating and processing the solution, the true cutting angle can be obtained. Satisfaction formula: .

[0049] Based on the above calculation process, it can be seen that the cutting angle of the fiber end face is related to the viewing tilt angles of the two cameras. After obtaining a more accurate and reliable first and second viewing tilt angles based on the above processing method, and then based on the above calculation process, i.e., the preset mathematical model... This allows us to calculate the true and accurate cutting angle.

[0050] The following image processing techniques are used to obtain the viewing tilt angles of the first camera (X camera) and the second camera (Y camera). Taking the first camera as an example, as follows... Figure 8 As shown, Figure 8 This is a schematic diagram of a first image acquired by a first camera according to an embodiment of this application. For the second camera, the subsequent processing is the same as the processing method of the first image acquired by the first camera.

[0051] S1 performs median filtering and morphological operations (erosion and dilation) on the image to denoise it. S2, Binarize the image by applying an appropriate threshold; S3, perform pixel statistics on the image in the X direction, select appropriate parameters within the discontinuity interval, and separate the left and right fiber images. The separation image can be shown as follows: Figure 9 and Figure 10 As shown; S4. Label the separated images as XL, XR, YL, and YR (X field of view LR, Y field of view LR). Perform corner detection on each image, which can be done using Harris corner detection or Shi-Tomasi corner detection. S5 filters and selects the acquired corner points. After filtering, the corner points can be labeled for use in tilt angle calculation. The corner points can be displayed as follows: Figure 11 As shown; S6, for the corner points obtained in S5, the upper and lower points are marked as P(x1,y1) and D(x2,y2) respectively. Since the tilt angle on the image is equivalent to the vertical angle, PD tilt angle compensation should be performed. And apply them to the above formulas to obtain the first tilt angle and the second tilt angle; S7. Apply the above calculation formula for the cutting angle to the first and second tilt angles obtained in S6 to calculate the actual cutting angle.

[0052] In summary, the above embodiments provide a method for detecting the cutting angle of an optical fiber end face. A first camera and a second camera, perpendicular to each other, acquire a first image of the optical fiber end face from a first viewing angle and a second image from a second viewing angle, respectively. The first and second images are processed to calculate the first and second viewing tilt angles of the optical fiber end face from the first and second viewing angles, respectively. Based on the first and second viewing tilt angles, the cutting angle of the optical fiber end face is calculated using a preset mathematical model. By employing dual cameras with perpendicularity for multi-view observation and combining this with a mathematical model based on three-dimensional geometric characteristics for cutting angle calculation, the accuracy and reliability of cutting angle detection are improved.

[0053] Based on the method described in the above embodiments, this embodiment will further describe the cutting angle detection device of the optical fiber end face. The cutting angle detection device of the optical fiber end face can be implemented as an independent entity or integrated into an electronic device, such as a terminal, which may include a mobile phone, tablet computer, etc.

[0054] Please see Figure 12 , Figure 12 This is a schematic diagram of a fiber optic end-face cutting angle detection device provided in an embodiment of this application, as shown below. Figure 12 As shown, the fiber optic end face cutting angle detection device 1200 provided in this application embodiment includes: The image acquisition module 1201 is used to acquire a first camera and a second camera that are perpendicular to each other, and respectively acquire a first image of the fiber end face from a first viewpoint and a second image from a second viewpoint. Image processing module 1202 is used to process the first image and the second image respectively, and calculate the first viewing tilt angle of the fiber end face under the first viewing angle and the second viewing tilt angle under the second viewing angle. The angle calculation module 1203 is used to calculate the cutting angle of the fiber end face based on the first tilt angle and the second tilt angle and a preset mathematical model.

[0055] In one embodiment, the image processing module 1202 is further configured to: Preprocessing is performed on the first image or the second image, including denoising the first image or the second image based on median filtering and morphological operations; Image segmentation processing is performed on the preprocessed first or second image to obtain the left fiber image and the right fiber image of the first or second image; By processing the left and right fiber images, the first apparent tilt angle of the fiber end face under the first viewing angle or the second apparent tilt angle under the second viewing angle is obtained.

[0056] In one embodiment, the image processing module 1202 is further configured to: The first or second image after preprocessing is binarized by applying a threshold, and the pixel statistics of the binarized image are obtained by performing pixel statistics in the horizontal direction; Based on the pixel statistics, the first image or the second image is segmented to obtain the left fiber image and the right fiber image corresponding to the first image or the second image.

[0057] In one embodiment, the image processing module 1202 is further configured to: Based on pixel statistics, the discontinuities from the background region to the fiber optic region and from the fiber optic region to the background region are identified. The left and right boundaries of the optical fiber are determined based on the discontinuity points, and the first or second image is segmented according to the left and right boundaries to obtain the left and right optical fiber images corresponding to the first or second image.

[0058] In one embodiment, the image processing module 1202 is further configured to: Corner detection is performed on the left and / or right fiber images to generate a set of corner points containing multiple potential edge feature points; Based on the corner point set, a robust straight line fitting algorithm using random sampling consensus is employed to obtain the best-fit straight line at the edge of the fiber optic cut surface. Based on the parameters of the best-fit straight line, the inclination of the fiber optic cut surface edge in the first or second image is determined, and the inclination is directionally corrected to calculate the first apparent tilt angle of the fiber end face in the first view or the second apparent tilt angle in the second view.

[0059] In one embodiment, the image processing module 1202 is further configured to: Corner points are filtered out by removing those whose distance from the fiber optic cut surface area is greater than a first threshold and those whose confidence level is lower than a second threshold.

[0060] In one embodiment, the image processing module 1202 is further configured to: Set the maximum number of iterations and the distance threshold; In each iteration, the first and second corner points are selected from the set of corner points to construct candidate line models; Calculate the distance from all corner points in the corner point set except for the first and second corner points to the candidate straight line model, and mark the corner points whose distance is less than the distance threshold as interior points; Record the number of interior points and proceed to the next iteration. After completing the maximum number of iterations, select the candidate line model with the most interior points as the best-fit line.

[0061] Additionally, please see Figure 13 , Figure 13 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device can be a mobile terminal such as a smartphone or tablet computer. Figure 13 As shown, the electronic device 1300 includes a processor 1301 and a memory 1302. The processor 1301 and the memory 1302 are electrically connected.

[0062] The processor 1301 is the control center of the electronic device 1300. It connects various parts of the electronic device through various interfaces and lines. By running or loading the application program stored in the memory 1302 and calling the data stored in the memory 1302, it performs various functions of the electronic device 1300 and processes data, thereby monitoring the electronic device 1300 as a whole.

[0063] In this embodiment, the processor 1301 in the electronic device 1300 will load the instructions corresponding to the processes of one or more applications into the memory 1302 according to the following steps, and the processor 1301 will run the applications stored in the memory 1302 to realize any step in the fiber end face cutting angle detection method provided in the above embodiment.

[0064] The electronic device 1300 can implement the steps of any embodiment of the fiber end face cutting angle detection method provided in the embodiments of this application. Therefore, it can achieve the beneficial effects that any fiber end face cutting angle detection method provided in the embodiments of this application can achieve. For details, please refer to the previous embodiments, which will not be repeated here.

[0065] Please see Figure 14 , Figure 14 This is another structural schematic diagram of the electronic device provided in the embodiments of this application, such as... Figure 14 As shown, Figure 14 A specific structural block diagram of an electronic device provided in an embodiment of this application is shown. This electronic device can be used to implement the fiber optic end face cutting angle detection method provided in the above embodiments. The electronic device 1400 can be a mobile terminal such as a smartphone or laptop computer.

[0066] RF circuit 1410 is used to receive and transmit electromagnetic waves, converting electromagnetic waves into electrical signals and vice versa, thereby enabling communication with communication networks or other devices. RF circuit 1410 may include various existing circuit elements used to perform these functions, such as antennas, radio frequency transceivers, digital signal processors, encryption / decryption chips, subscriber identity modules (SIM cards), memory, etc. RF circuit 1410 can communicate with various networks such as the Internet, corporate intranets, and wireless networks, or communicate with other devices via wireless networks. The aforementioned wireless networks may include cellular telephone networks, wireless local area networks (WLANs), or metropolitan area networks (MANs). The aforementioned wireless networks may use various communication standards, protocols, and technologies, including but not limited to Global System for Mobile Communication (GSM), Enhanced Data GSM Environment (EDGE), Wideband Code Division Multiple Access (WCDMA), Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Wireless Fidelity (Wi-Fi) (such as IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, and / or IEEE 802.11n), Voice over Internet Protocol (VoIP), Worldwide Interoperability for Microwave Access (Wi-Max), other protocols for email, instant messaging, and short messages, and any other suitable communication protocols, including those that have not yet been developed.

[0067] The memory 1420 can be used to store software programs and modules, such as the program instructions / modules corresponding to the fiber end face cutting angle detection method in the above embodiment. The processor 1480 executes various functional applications and the fiber end face cutting angle detection method by running the software programs and modules stored in the memory 1420.

[0068] Memory 1420 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, memory 1420 may further include memory remotely located relative to processor 1480, which can be connected to electronic device 1400 via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0069] Input unit 1430 can be used to receive uploaded digital or character information, and to generate keyboard, mouse, joystick, optical, or trackball signal inputs related to user settings and function control. Specifically, input unit 1430 may include a touch-sensitive surface 1431 and other input devices 1432. Touch-sensitive surface 1431, also known as a touch display or touchpad, can collect user touch operations on or near it (such as user operations using fingers, styluses, or any suitable object or accessory on or near touch-sensitive surface 1431), and drive corresponding connection devices according to a pre-set program. Optionally, touch-sensitive surface 1431 may include two parts: a touch detection device and a touch controller. The touch detection device detects the user's touch position and the signal generated by the touch operation, and transmits the signal to the touch controller; the touch controller receives touch information from the touch detection device, converts it into touch point coordinates, sends it to the processor 1480, and can receive and execute commands from the processor 1480. In addition, the touch-sensitive surface 1431 can be implemented using various types such as resistive, capacitive, infrared, and surface acoustic wave. Besides the touch-sensitive surface 1431, the input unit 1430 may also include other input devices 1432. Specifically, other input devices 1432 may include, but are not limited to, one or more of the following: a physical keyboard, function keys (such as volume control buttons, power buttons, etc.), a trackball, a mouse, and a joystick.

[0070] Display unit 1440 can be used to display information input by the user or information provided to the user, as well as various graphical user interfaces of electronic device 1400. These graphical user interfaces can be composed of graphics, text, icons, video, and any combination thereof. Display unit 1440 may include display panel 1441, optionally configured as LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), or other similar forms. Further, touch-sensitive surface 1431 may cover display panel 1441. When touch-sensitive surface 1431 detects a touch operation on or near it, it transmits the information to processor 1480 to determine the type of touch event. Subsequently, processor 1480 provides corresponding visual output on display panel 1441 according to the type of touch event. Although in the figures, touch-sensitive surface 1431 and display panel 1441 are implemented as two separate components to achieve input and output functions, in some embodiments, touch-sensitive surface 1431 and display panel 1441 can be integrated to achieve input and output functions.

[0071] The electronic device 1400 may also include at least one sensor 1450, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor can adjust the brightness of the display panel 1441 according to the ambient light level, and the proximity sensor can generate an interruption when the flip is closed or shut down. As a type of motion sensor, a gravity acceleration sensor can detect the magnitude of acceleration in various directions (generally three axes), and can detect the magnitude and direction of gravity when stationary. It can be used for applications that recognize the phone's posture (such as landscape / portrait switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc. Other sensors that may be configured in the electronic device 1400, such as gyroscopes, barometers, hygrometers, thermometers, and infrared sensors, will not be described in detail here.

[0072] Audio circuitry 1460, speaker 1461, and microphone 1462 provide an audio interface between the user and electronic device 1400. Audio circuitry 1460 converts received audio data into electrical signals and transmits them to speaker 1461, where speaker 1461 converts them into sound signals for output. Conversely, microphone 1462 converts collected sound signals into electrical signals, which are then received by audio circuitry 1460, converted back into audio data, processed by processor 1480, and then transmitted via RF circuitry 1410 to, for example, another terminal, or output to memory 1420 for further processing. Audio circuitry 1460 may also include an earphone jack to facilitate communication between peripheral headphones and electronic device 1400.

[0073] Electronic device 1400, through transmission module 1470 (e.g., Wi-Fi module), can help users receive requests, send information, etc., providing users with wireless broadband internet access. Although transmission module 1470 is shown in the figure, it is understood that it is not an essential component of electronic device 1400 and can be omitted as needed without changing the essence of the invention.

[0074] The processor 1480 is the control center of the electronic device 1400. It connects to various parts of the phone via various interfaces and lines. By running or executing software programs and / or modules stored in the memory 1420, and by calling data stored in the memory 1420, it performs various functions and processes data of the electronic device 1400, thereby providing overall monitoring of the electronic device. Optionally, the processor 1480 may include one or more processing cores; in some embodiments, the processor 1480 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may also not be integrated into the processor 1480.

[0075] Electronic device 1400 also includes a power supply 1490 (such as a battery) for supplying power to various components. In some embodiments, the power supply may be logically connected to processor 1480 through a power management system, thereby enabling functions such as managing charging, discharging, and power consumption through the power management system. Power supply 1490 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0076] Although not shown, the electronic device 1400 also includes cameras (such as front-facing cameras and rear-facing cameras), Bluetooth modules, etc., which will not be described in detail here. Specifically, in this embodiment, the display unit of the electronic device is a touch screen display, and the mobile terminal also includes a memory and one or more programs, one or more of which are stored in the memory and configured to be executed by one or more processors to implement any step of the fiber optic end face cutting angle detection method provided in the above embodiment.

[0077] In practice, the above modules can be implemented as independent entities or combined in any way to be implemented as the same or several entities. For the specific implementation of the above modules, please refer to the previous method implementation examples, which will not be repeated here.

[0078] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor. Therefore, this application provides a storage medium storing multiple instructions that, when executed by a processor, can implement any step in the fiber optic end-face cutting angle detection method provided in the above embodiments.

[0079] The storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0080] Since the instructions stored in the storage medium can execute the steps in any embodiment of the fiber end face cutting angle detection method provided in this application, the beneficial effects that any fiber end face cutting angle detection method provided in this application can achieve can be realized. For details, please refer to the previous embodiments, which will not be repeated here.

[0081] The foregoing has provided a detailed description of a method, apparatus, electronic device, and storage medium for detecting the cutting angle of an optical fiber end face, as provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application. Moreover, those skilled in the art can make several improvements and modifications without departing from the principles of this application, and these improvements and modifications are also considered to be within the scope of protection of this application.

Claims

1. A method for detecting the cutting angle of an optical fiber end face, characterized in that, The method includes: A first and a second camera, which are perpendicular to each other, are used to acquire a first image of the fiber end face from a first viewpoint and a second image from a second viewpoint, respectively. The first image and the second image are processed respectively to calculate the first viewing tilt angle of the fiber end face under the first viewing angle and the second viewing tilt angle under the second viewing angle. The cutting angle of the fiber end face is calculated based on the first apparent tilt angle and the second apparent tilt angle, using a preset mathematical model.

2. The method for detecting the cutting angle of the optical fiber end face as described in claim 1, characterized in that, The step of processing the first image and the second image respectively to calculate the first tilt angle of the fiber end face under the first viewing angle and the second tilt angle under the second viewing angle includes: The first image or the second image is preprocessed, and the preprocessing includes denoising the first image or the second image based on median filtering and morphological operations; Image segmentation processing is performed on the preprocessed first image or second image to obtain the left fiber image and the right fiber image of the first image or second image; The left and right fiber images are processed to obtain the first viewing tilt angle of the fiber end face under the first viewing angle or the second viewing tilt angle under the second viewing angle.

3. The method for detecting the cutting angle of the optical fiber end face as described in claim 2, characterized in that, The step of performing image segmentation processing on the preprocessed first or second image to obtain the left fiber image and the right fiber image of the first or second image includes: The first or second image after preprocessing is binarized by applying a threshold, and the pixel statistics of the binarized image are obtained by performing pixel statistics in the horizontal direction; Based on the pixel statistics, the first image or the second image is segmented to obtain the left fiber image and the right fiber image corresponding to the first image or the second image.

4. The method for detecting the cutting angle of the optical fiber end face as described in claim 3, characterized in that, The step of segmenting the first image or the second image based on the pixel statistics results to obtain the left fiber image and the right fiber image corresponding to the first image or the second image includes: Based on the pixel statistics, the discontinuities from the background region to the fiber region and from the fiber region to the background region are identified. The left and right boundaries of the optical fiber are determined based on the discontinuity points, and the first or second image is segmented according to the left and right boundaries to obtain the left and right optical fiber images corresponding to the first or second image.

5. The method for detecting the cutting angle of the optical fiber end face as described in claim 2, characterized in that, The step of processing the left fiber image and the right fiber image to obtain the first tilt angle of the fiber end face under the first viewing angle or the second tilt angle under the second viewing angle includes: Corner detection is performed on the left fiber image and / or the right fiber image to generate a corner set containing multiple potential edge feature points; Based on the set of corner points, a robust straight line fitting is performed using a random sampling consensus algorithm to obtain the best-fit straight line at the edge of the fiber optic cut surface. Based on the parameters of the best-fit straight line, the tilt of the fiber optic cut surface edge in the first image or the second image is determined, and the tilt is directionally corrected to calculate the first viewing tilt angle of the fiber optic end face in the first viewing angle or the second viewing tilt angle in the second viewing angle.

6. The method for detecting the cutting angle of the optical fiber end face as described in claim 5, characterized in that, Before obtaining the best-fitting straight line for the fiber optic cut surface edge by performing robust straight line fitting using a random sampling consensus algorithm based on the corner point set, the method further includes: The corner points are filtered out by removing those whose distance from the fiber optic cutting surface area is greater than a first threshold and those whose confidence level is lower than a second threshold.

7. The method for detecting the cutting angle of the optical fiber end face as described in claim 5, characterized in that, The step of obtaining the best-fitting straight line for the fiber optic cut surface edge by performing robust straight line fitting using a random sampling consensus algorithm based on the corner point set includes: Set the maximum number of iterations and the distance threshold; In each iteration, a first corner point and a second corner point are selected from the set of corner points to construct a candidate straight line model; Calculate the distance from all corner points in the corner point set except for the first corner point and the second corner point to the candidate line model, and mark the corner points whose distance is less than the distance threshold as interior points; Record the number of interior points and proceed to the next iteration. After completing the maximum number of iterations, select the candidate line model with the most interior points as the best-fit line.

8. A device for detecting the cutting angle of an optical fiber end face, characterized in that, include: The image acquisition module is used to acquire images from a first camera and a second camera that are perpendicular to each other, respectively capturing a first image of the fiber end face from a first perspective and a second image from a second perspective. An image processing module is used to process the first image and the second image respectively, and calculate the first viewing tilt angle of the fiber end face under the first viewing angle and the second viewing tilt angle under the second viewing angle; An angle calculation module is used to calculate the cutting angle of the fiber end face based on the first apparent tilt angle and the second apparent tilt angle, using a preset mathematical model.

9. An electronic device, characterized in that, The electronic device includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the fiber optic end face cutting angle detection method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the fiber optic end face cutting angle detection method as described in any one of claims 1 to 7.

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