Calibration-plate-free zoom camera self-calibration method and system based on deep learning

Abstract

The invention discloses a calibration-plate-free zoom camera self-calibration method and system based on deep learning, and belongs to the technical field of camera calibration, and the method comprises the steps: obtaining image groups with the same visual angle and different focal lengths and image groups with the same focal length and different visual angles; performing feature point matching on the two image groups by using an improved lightweight ASTR algorithm to obtain a first group of matching point pairs and a second group of matching point pairs; on the basis of the first group of matching point pairs, a least square method is adopted to calculate zoom centers of the image groups with the same visual angle and different focal lengths; calculating a camera internal reference matrix of the image groups with the same focal length and different visual angles based on the second group of matching point pairs, performing polynomial fitting based on the camera internal reference matrix, and solving a polynomial to obtain a camera internal reference value; and completing self-calibration of the zoom camera based on the obtained zoom center and the internal reference value of the camera. According to the method, the calculation complexity is reduced, the applicable scene range of the method is expanded, and the calibration precision and robustness are effectively improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of camera calibration, and particularly relates to a calibration board-free zoom camera self-calibration method and system based on deep learning. Background Art

[0002] Camera calibration is a key step in obtaining three-dimensional spatial depth information from two-dimensional images and plays an important role in multiple fields such as computer vision, robotics, and autonomous driving. Through precise calibration, the internal and external parameters of the camera can be determined, thereby enabling high-precision three-dimensional reconstruction, target recognition, and tracking tasks. Camera calibration is not only the basis for achieving accurate depth perception but also a key link in ensuring the performance of various vision systems. Nowadays, the calibration methods for fixed-focus cameras are relatively mature and can meet the requirements of most applications. However, in modern image processing and photography applications, multi-focus cameras are widely used in consumer devices (such as single-lens reflex cameras and mobile phones) and industrial devices (such as drones and surveillance systems). In the case of multiple focal lengths, due to the dynamic changes in the camera's internal parameters, distortion coefficients, and optical center displacement caused by the focal length change, it poses a great challenge to camera calibration and subsequent calculations.

[0003] Currently, although there are available parameter models for zoom camera calibration, its research is usually based on high-precision camera calibration methods using calibration boards and is not applicable to daily or dynamic scenarios. Therefore, the self-calibration of zoom cameras is an important research direction. Among the current self-calibration methods, techniques based on vanishing points or Aruco codes rely on specific geometric characteristics of the scene and are difficult to meet the requirements of dynamic or complex scenarios; although the methods based on feature matching get rid of the limitation of the target, most of them use traditional matching algorithms (such as SIFT), which have high computational complexity, weak robustness, and require additional post-processing to eliminate mismatched point pairs, resulting in complex operations and low efficiency. The ASTR algorithm based on deep learning can handle complex or dynamic scenarios and can also adapt to large-scale changes, but the model is relatively complex and the amount of calculation is large.

[0004] Therefore, it is necessary to design a calibration board-free zoom camera self-calibration method and system based on deep learning to address the above problems. Summary of the Invention

[0005] The object of the present invention is to address the problems existing in the prior art and provide a calibration board-free zoom camera self-calibration method and system based on deep learning. By proposing an improved lightweight ASTR algorithm and combining the prior knowledge of the change in the internal parameters of the zoom camera, the polynomial fitting method is optimized, which not only reduces the deviation caused by noise and non-linear errors in the traditional method but also effectively improves the calibration accuracy and robustness, expands the applicable scene range of the method, and introduces deep learning feature matching and the least squares method into the calibration of the image scaling center, effectively improving the calibration accuracy and robustness.

[0006] According to one aspect of the present specification, a calibration-free zoom camera self-calibration method based on deep learning is provided, including:

[0007] Based on the captured images, group the images to obtain image groups with the same viewing angle and different focal lengths, and image groups with the same focal length and different viewing angles;

[0008] Use the improved lightweight adaptive point-guided transformer model to perform feature point matching on the two image groups to obtain the first set of matching point pairs and the second set of matching point pairs;

[0009] Based on the first set of matching point pairs, use the least squares method to calculate the scaling center of the image groups with the same viewing angle and different focal lengths; based on the second set of matching point pairs, calculate the camera internal parameter matrix of the image groups with the same focal length and different viewing angles, and perform polynomial fitting based on the camera internal parameter matrix to solve the polynomial to obtain the camera internal parameter values;

[0010] Based on the scaling center of the image groups with the same viewing angle and different focal lengths and the camera internal parameter values of the image groups with the same focal length and different viewing angles, complete the self-calibration of the camera.

[0011] Further, the improvement of the lightweight adaptive point-guided transformer model (ASTR) includes:

[0012] Based on the backbone network of the mobile convolutional neural network V4 (MobileNetV4), introduce the inverted bottleneck block (UIB) and the depthwise separable convolution module;

[0013] Replace the feature pyramid network (FPN) module of the backbone network with the MobileNetV4 - feature pyramid network module;

[0014] In the spot-guided aggregation module of the adaptive point-guided transformer (ASTR), introduce the dual cross-attention mechanism (DCA), reduce the cross-sampling layer from four layers to two layers, and change the scale of the coarse-grained matching from to .

[0015] Further, calculating the camera internal parameter matrix of the image groups with the same focal length and different viewing angles includes:

[0016] Based on the second set of matching point pairs, estimate the fundamental matrix of the zoom camera, and calculate the projection matrix according to the fundamental matrix;

[0017] The absolute quadric constraint corresponding to the projection matrix is solved by using the quasi-linear method to obtain the camera intrinsic matrix of image groups with the same focal length and different viewpoints.

[0018] Further, the camera intrinsic values are obtained by solving the polynomial, including:

[0019] Based on the camera intrinsic matrix, polynomial fitting is performed to construct a linear equation system;

[0020] The linear equation system is solved by the least squares method to obtain the camera intrinsic values of the zoom camera.

[0021] Further, the scaling centers of image groups with the same viewpoint and different focal lengths are calculated, including:

[0022] Based on each pair of matching points in the first group of matching point pairs, a vector equation is formulated;

[0023] The vector equation is solved by the least squares method to obtain the scaling center values of image groups with the same viewpoint and different focal lengths.

[0024] According to one aspect of the present specification, a deep learning-based calibration-free zoom camera self-calibration system is provided, including:

[0025] An image grouping module for grouping the captured images to obtain image groups with the same viewpoint and different focal lengths and image groups with the same focal length and different viewpoints;

[0026] A feature point matching module for using an improved lightweight adaptive point-guided transformer model to perform feature point matching on the two image groups to obtain a first group of matching point pairs and a second group of matching point pairs;

[0027] A parameter calculation module for calculating the scaling centers of image groups with the same viewpoint and different focal lengths based on the first group of matching point pairs by using the least squares method; calculating the camera intrinsic matrix of image groups with the same focal length and different viewpoints based on the second group of matching point pairs, and performing polynomial fitting based on the camera intrinsic matrix to solve the polynomial to obtain the camera intrinsic values;

[0028] A camera self-calibration module for completing the self-calibration of the camera based on the scaling centers of image groups with the same viewpoint and different focal lengths and the camera intrinsic values of image groups with the same focal length and different viewpoints.

[0029] According to one aspect of the present specification, an electronic device is provided, including a memory and a processor, the memory storing a computer program, characterized in that when the processor executes the computer program, the steps of the deep learning-based calibration-free zoom camera self-calibration method are implemented.

[0030] According to one aspect of the present specification, there is provided a computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the self-calibration method of the zoom camera based on deep learning are implemented.

[0031] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0032] 1. The present invention processes two different sets of image group data separately obtained by a zoom camera, improving the data usage efficiency and reducing computational redundancy.

[0033] 2. In view of the characteristics of large-scale change images (such as image groups with different focal lengths or perspectives) involved in the self-calibration of zoom cameras, the present invention proposes an improved lightweight ASTR algorithm, which replaces the feature matching part in the conventional camera self-calibration method. This not only reduces the computational complexity, significantly improves the calibration accuracy, but also expands the applicable scenario range of the method.

[0034] 3. The present invention optimizes the polynomial fitting method in combination with the prior knowledge of the change of the internal parameters of the zoom camera. While ensuring the fitting accuracy and robustness, it simplifies the calculation process of the zoom internal parameters, reduces the computational complexity, and better adapts to the actual application requirements of zoom camera calibration.

[0035] 4. The present invention introduces deep learning feature matching and the least squares method into the calibration of the image scaling center, effectively improving the calibration accuracy and robustness, and reducing the deviation caused by noise and non-linear errors in the conventional method. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] 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 following drawings are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings without creative efforts based on these drawings.

[0037] Figure 1 It is a flowchart of the self-calibration method of the zoom camera according to the embodiment of the present invention;

[0038] Figure 2 It is a schematic diagram of the improved ASTR algorithm according to the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0040] The present invention provides a calibration-free zoom camera self-calibration method based on deep learning, as Figure 1 shown, including: grouping the captured images to obtain image groups with the same viewing angle and different focal lengths and image groups with the same focal length and different viewing angles; using an improved lightweight adaptive point-guided transformer model to perform feature point matching on the two image groups to obtain a first set of matching point pairs and a second set of matching point pairs; based on the first set of matching point pairs, using the least squares method to calculate the scaling center of the image groups with the same viewing angle and different focal lengths; based on the second set of matching point pairs, calculating the camera internal parameter matrix of the image groups with the same focal length and different viewing angles, and performing polynomial fitting based on the camera internal parameter matrix to solve the polynomial to obtain the camera internal parameter values; based on the scaling center of the image groups with the same viewing angle and different focal lengths and the camera internal parameter values of the image groups with the same focal length and different viewing angles, complete the self-calibration of the camera.

[0041] Specifically, the main purpose of the embodiments of the present invention is to calibrate the scaling center of the zoom camera and the internal parameter matrix of the zoom camera , and the matrix has the following expression:

[0042] (1)

[0043] where , are the focal lengths of the camera in the X and Y directions, is the pixel shear coefficient, , are the positions of the principal point in the image coordinate system. According to the characteristics of the zoom camera, each parameter in the internal parameter matrix changes with the focal length and can be expressed as a polynomial function, and the expression is as follows:

[0044] (2)

[0045] where is the polynomial coefficient corresponding to the parameter , and needs to be determined through the calibration process.

[0046] Specifically, the key task of calibration is to determine the polynomial coefficients of each parameter through feature point matching and mathematical fitting , so as to achieve the accurate calculation and representation of the internal parameter matrix at any focal length .

[0047] Specifically, the embodiment of the present invention also provides data acquisition based on the fixed interval change of the focal length. At least 5 shooting angles are preset and the camera is fixed. Each angle is set with no less than 5 focal lengths, and the focal lengths include the maximum and minimum focal lengths within the adjustable range of the zoom camera, and the interval is fixed. The captured images are used as experimental shooting data. At each preset angle, the focal length is adjusted from the minimum to the maximum in sequence for shooting. After the image acquisition of all focal lengths is completed, switch to the next angle and repeat the operation until the image acquisition of all angle and focal length combinations is completed. The captured images are classified and sorted. Among them, the images with different focal lengths under the same angle are used to calculate the image scaling center of the zoom camera, and the angle and focal length parameters of each image are recorded to ensure the integrity and accuracy of the data. The images with different angles under the same focal length are used to calculate the camera internal parameters at a specific focal length, and the influence of the focal length change on the camera internal parameters is analyzed.

[0048] Specifically, the embodiment of the present invention also provides using the improved ASTR algorithm for feature point matching, preprocessing the collected images, and adjusting the images to the input format required by the algorithm. Use the improved ASTR algorithm to extract image features, and the improved ASTR algorithm is as Figure 2 shown. The specific improvement is as follows: Referring to the backbone network of MobileNetV4, an inverted bottleneck block (UIB) and a depthwise separable convolution module are introduced, and combined with the feature pyramid network (FPN) in the original CNN module, the convolutional feature extraction module is replaced with a lighter MobileNetV4-FPN module. In the Spot-Guided Aggregation module, a dual cross-attention mechanism (DCA) is introduced, which includes two parts: channel cross-attention (CCA) and spatial cross-attention (SCA). The number of cross-sampling layers is reduced from four layers to two layers, and the scale of coarse-grained matching is changed from to . While retaining the original core structure and optimizing the attention mechanism, the model calculation amount is significantly reduced. According to the type of the collected images, an indoor model is trained on the ScanNet dataset, and an outdoor model is trained on the MegaDepth dataset. Prune the trained model and convert it to the ONNX format. The lightweight model can be transplanted to edge devices. Combine the denoising and robustness optimization algorithms to screen the matching point pairs and output the optimized feature matching results , and the expression is as follows:

[0049] (3)

[0050] wherein, and They are feature points in homogeneous coordinate form, , which are the feature points The abscissa and ordinate on the image plane, , which are the feature points The abscissa and ordinate on the image plane.

[0051] Specifically, the embodiments of the present invention also provide the calculation of the internal parameters for the fixed interval change of the focal length. Using the feature matching point pairs at the same focal length and different viewpoints To calculate the internal parameters of the zoom camera. First, use the matching point pairs and estimate the fundamental matrix by the 8-point method , which satisfies the following conditions:

[0052] (4)

[0053] Among them, Is a 3×3 matrix that describes the epipolar geometry relationship between two images. Then, the epipoles of the two images need to be found , and the epipole Satisfies the conditions of equation (5):

[0054] (5)

[0055] Among them, , And Are all the epipolar lines in the epipolar geometry. Ideally, the point Passes through all the epipolar lines . In actual operation, a point Will be calculated to make The sum of the distances to each line is the smallest, and the least squares method is used to solve it, as shown in equation (6):

[0056] (6)

[0057] After calculating , , solve for the projection matrix According to equation (7):

[0058] (7)

[0059] Among them, Is The identity matrix of, And Correspond to the camera projection matrices at the same focal length and different viewpoints.

[0060] Specifically, a class of surfaces in the three-dimensional projective space that satisfy Equation (8) is called a quadric surface. Among them, is a matrix of

[0061] (8)

[0062] Among them, refers to the four-dimensional homogeneous coordinate form of a certain point in the three-dimensional space, is the transpose matrix of

[0063] (9)

[0064] Among them, is the absolute conic, that is, in Equation (7), is the transpose matrix of and thus

[0065] (10)

[0066] Among them, are the matrix element values of the absolute quadric surface The projection constraint of the absolute quadric surface is the basis of the self-calibration method, and its constraint is as Equation (11):

[0067] (11)

[0068] Among them, is the anti-symmetrization operation, is the absolute conic, is the absolute quadric surface.

[0069] Specifically, to solve the constraint (11) using the quasi-linear method, first express the independent components of and as vectors and construct an outer product matrix. The dimension of this matrix should be . This matrix represents the projection constraint and is linearly dependent among these variables with a rank of 15. Then, use the image data of more than 5 images to linearly recover this matrix. Next, use the singular value decomposition (SVD) of and to project the matrix into a rank-1 space and decompose it into vectors has a rank of 3 and is adjusted by setting its minimum eigenvalue to zero , thus completing the solution for and . After calculating , the camera intrinsic matrix is obtained using the Cholesky decomposition method, as shown in Equation (12):

[0070] (12)

[0071] Specifically, the embodiment of the present invention also provides a functional relationship between the polynomial fitting of the intrinsic parameters with respect to the focal length. Denote the camera intrinsic parameters at different focal lengths as , is the focal length of the camera. For the accuracy of polynomial fitting, should include the maximum and minimum focal lengths that the zoom camera can adjust. A linear equation system is constructed, as shown in Equation (13):

[0072] (13)

[0073] In practical applications, according to experience, when in the highest degree is 2, the requirements can be met in the vast majority of cases.

[0074] (14)

[0075] Among them, is the design matrix, are the coefficients to be determined for the polynomial, is the vector of observed intrinsic parameter values. After obtaining the linear equation system (13), the coefficients of the five degrees of freedom of the intrinsic matrix are solved by the least squares method:

[0076] (15)

[0077] The obtained polynomial is . Using this expression, the intrinsic parameter values at any focal length within the focal length range can be predicted:

[0078] (16)

[0079] Specifically, the embodiment of the present invention also provides a method for calibrating the image scaling center based on the least squares method. Using the feature matching point pairs at the same viewing angle and different focal lengths obtained by the improved ASTR algorithm in step two, the image scaling center of the zoom camera is calculated. If there are pairs of matching points, according to the fact that two points determine a straight line equation, pairs of matching points can determine A straight-line equation. The straight-line equation determined by each pair of matching points is:

[0080] (17)

[0081] Among them, Equation (17) is the basic form of the straight-line equation in the plane of dimension, that is .

[0082] Zoom center The coordinates of are , for an ideal zoom lens, its zoom center satisfies the straight-line equation determined by any matching pair. Represented by a mathematical expression, as Equation (18):

[0083] (18)

[0084] Writing Equation (18) in matrix form gives:

[0085] (19)

[0086] Writing Equation (19) in a one-to-one correspondence and abbreviating it gives:

[0087] (20)

[0088] Using the least squares method to solve Equation (20) to obtain the estimator of the zoom center:

[0089] (21)

[0090] Among them, represents the estimator solved by the least squares method.

[0091] Finally, the zoom center of the zoom camera can be calculated from Equation (21).

[0092] The implementation basis of each embodiment of the present invention is achieved through programmed processing by a device with processor functions. Therefore, in engineering practice, the technical solutions and functions of each embodiment of the present invention are encapsulated into various modules. Based on this actual situation, on the basis of the above embodiments, the embodiments of the present invention provide a self-calibration system for a calibration-free zoom camera based on deep learning, and this system is used to execute a self-calibration method for a calibration-free zoom camera based on deep learning in the above method embodiments.

[0093] The system includes: an image grouping module, which is used to group the captured images to obtain image groups with the same viewing angle but different focal lengths and image groups with the same focal length but different viewing angles; a feature point matching module, which is used to perform feature point matching on the two image groups by using an improved lightweight adaptive point-guided transformer model to obtain a first group of matching point pairs and a second group of matching point pairs; a parameter calculation module, which is used to calculate the scaling center of the image groups with the same viewing angle but different focal lengths by using the least squares method based on the first group of matching point pairs; calculate the camera internal parameter matrix of the image groups with the same focal length but different viewing angles based on the second group of matching point pairs, and perform polynomial fitting based on the camera internal parameter matrix to solve the polynomial to obtain the camera internal parameter values; a camera self-calibration module, which is used to complete the self-calibration of the camera based on the scaling center of the image groups with the same viewing angle but different focal lengths and the camera internal parameter values of the image groups with the same focal length but different viewing angles.

[0094] An uncalibrated board zoom camera self-calibration system based on deep learning provided by an embodiment of the present invention addresses the problems of low accuracy and stability in feature matching and calibration methods in camera self-calibration methods. By using the above-mentioned several modules, two different sets of image group data are obtained through a zoom camera and processed separately. The improved lightweight ASTR algorithm and the least squares method are proposed, which not only reduce the computational complexity, significantly improve the calibration accuracy, but also expand the applicable scenario range of the method.

[0095] Based on the same inventive concept as the foregoing embodiment, an embodiment of the present invention also provides an electronic device, including a memory and a processor. The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the uncalibrated board zoom camera self-calibration method based on deep learning proposed in the above embodiment.

[0096] An embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it is used to reduce the deviation caused by noise and non-linear errors in traditional methods, improve the data usage efficiency, reduce computational redundancy, and improve the calibration accuracy. The storage medium can be any non-volatile storage device such as a hard disk, a solid-state drive, a flash drive, or an optical disc, and is used to store computer program code and necessary data files. The stored computer program includes: an image grouping module, a feature point matching module, a parameter calculation module, and a camera self-calibration module.

[0097] Finally, it should be noted that the above specific embodiments are only relatively representative examples of the present invention. Obviously, the present invention is not limited to the above specific embodiments and can have many variations. Any simple modification, equivalent change, and modification made to the above specific embodiments based on the technical essence of the present invention shall be considered to fall within the protection scope of the present invention.

Claims

1. A calibration plate-free zoom camera self-calibration method based on deep learning, characterized in that: include: Based on the captured images, grouping is performed to obtain image groups with the same viewing angle and different focal lengths and image groups with the same focal length and different viewing angles; The improved lightweight adaptive point-guided transformer model is used to match the feature points of the two image groups to obtain the first group of matching point pairs and the second group of matching point pairs; Based on the first set of matching point pairs, the least square method is used to calculate the zoom center of the image group with the same viewing angle and different focal lengths; Based on the second group of matching point pairs, a camera intrinsic parameter matrix of the image group with the same focal length and different viewing angles is calculated, and a polynomial fitting is performed based on the camera intrinsic parameter matrix to solve the polynomial to obtain a camera intrinsic parameter value; The camera self-calibration is completed based on the zoom center of the image group with the same viewing angle and different focal lengths and the camera intrinsic parameter values ​​of the image group with the same focal length and different viewing angles.

2. The method for self-calibration of a zoom camera without a calibration plate based on deep learning according to claim 1, characterized in that: Improvements to the lightweight adaptive point-guided transformer model include: Based on the backbone network of mobile convolutional neural network V4, the inverted bottleneck block and depth-separable convolution module are introduced; The feature pyramid network module of the backbone network is replaced by a mobile convolutional neural network V4-feature pyramid network module; In the point-guided aggregation module of the adaptive point-guided transformer, a double cross-attention mechanism is introduced to reduce the number of cross-sampling layers from four to two and reduce the scale of coarse-grained matching from Change to .

3. The method for self-calibration of a zoom camera without a calibration plate based on deep learning according to claim 1, characterized in that: The camera intrinsic parameter matrix of the image group with the same focal length and different viewing angles is calculated, including: Based on the second set of matching point pairs, the basic matrix of the zoom camera is estimated, and the projection matrix is ​​calculated according to the basic matrix; The absolute quadratic surface constraint corresponding to the projection matrix is ​​solved by a quasi-linear method, and the camera intrinsic parameter matrix of the image group with the same focal length and different viewing angles is obtained.

4. The method for self-calibration of a zoom camera without a calibration plate based on deep learning according to claim 1, characterized in that: The camera intrinsic parameter values ​​are obtained by solving the polynomial, including: Perform polynomial fitting based on the camera's intrinsic parameter matrix to construct a linear equation system; The linear equations are solved by the least square method to obtain the camera intrinsic parameters of the zoom camera.

5. The method for self-calibration of a zoom camera without a calibration plate based on deep learning according to claim 1, characterized in that: Calculate the zoom center of the image group with the same viewing angle and different focal lengths, including: Formulate a vector equation based on each matching point pair in the first set of matching point pairs; The least square method is used to solve the vector equation to obtain the zoom center value of the image group with the same viewing angle and different focal lengths.

6. A calibration plate-free zoom camera self-calibration system based on deep learning, characterized in that: include: An image grouping module is used to group the captured images into image groups with the same viewing angle and different focal lengths and image groups with the same focal length and different viewing angles; A feature point matching module is used to perform feature point matching on two image groups by using an improved lightweight adaptive point-guided transformer model to obtain a first group of matching point pairs and a second group of matching point pairs; A parameter calculation module, for calculating the zoom center of the image group with the same viewing angle and different focal lengths by using the least square method based on the first group of matching point pairs; Based on the second group of matching point pairs, a camera intrinsic parameter matrix of the image group with the same focal length and different viewing angles is calculated, and a polynomial fitting is performed based on the camera intrinsic parameter matrix to solve the polynomial to obtain a camera intrinsic parameter value; The camera self-calibration module is used to complete the camera self-calibration based on the zoom center of the image group with the same viewing angle and different focal lengths and the camera internal parameter values ​​of the image group with the same focal length and different viewing angles.

7. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the calibration plate-free zoom camera self-calibration method based on deep learning are implemented as described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the calibration plate-free zoom camera self-calibration method based on deep learning are implemented as described in any one of claims 1 to 5.

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