CN113029214A - Speckle sensing system based on multi-ring core optical fiber and speckle identification method - Google Patents
Speckle sensing system based on multi-ring core optical fiber and speckle identification method Download PDFInfo
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Abstract
本发明公开了一种基于多环芯光纤的散斑传感系统以散斑识别方法，其中散斑传感系统包括激光器、光场激发模组和多环芯光纤；激光器连接光场激发模组的输入端，光场激发模组的输出端连接多环芯光纤的首端，多环芯光纤的末端用于连接CCD相机，通过CCD相机采集多环芯光纤受扰动时散斑变换的散斑视频。本发明通过利用多环芯光纤散斑稳定、形态结构明显的特性以及对不同方位的扰动产生不同形变使得接收到的散斑变化有所不同的特点，实现了对扰动位置以及扰动方向多维度识别检测。同时，本发明只需对少量输出的散斑图像样本进行卷积神经网络的训练，且能够实现扰动位置和扰动方向的多维度检测。
The invention discloses a speckle sensing system based on a multi-ring core optical fiber and a speckle identification method, wherein the speckle sensing system comprises a laser, an optical field excitation module and a multi-ring core optical fiber; the laser is connected to the optical field excitation module The input end of the optical field excitation module is connected to the head end of the multi-ring core fiber, and the end of the multi-ring core fiber is used to connect the CCD camera. video. The invention realizes the multi-dimensional identification of the disturbance position and the disturbance direction by utilizing the characteristics of stable speckle and obvious morphological structure of the multi-ring core fiber and the characteristics that the received speckle changes are different due to the different deformation caused by the disturbance in different directions. detection. At the same time, the present invention only needs to train a convolutional neural network on a small number of output speckle image samples, and can realize multi-dimensional detection of disturbance position and disturbance direction.
Description
Technical Field
The invention relates to the technical field of optical sensing, in particular to a speckle sensing system and a speckle identification method based on multi-ring core optical fiber.
Background
In recent years, the rapid development of high-speed imaging, light wave shaping, and digital signal processing techniques has renewed interest in multimode fiber speckle sensing applications. Multimode fiber speckle sensors can provide greater capabilities and performance than single mode fiber sensors, taking advantage of their rich spatial mode diversity. However, applying a perturbation to a multimode fiber at a certain orientation usually causes a random distribution of energy between the multimode fiber modes, and the mathematical relationship is complicated to calculate. Therefore, it is difficult to establish an accurate mapping relationship between the acting orientation of the disturbance on the optical fiber and the speckle of the optical fiber. In order to solve the problems, the mainstream mode at present is to collect and train the speckle pattern of the multimode optical fiber through a convolutional neural network, and identify and classify the speckle pattern, so that the discrimination of the disturbance position on the optical fiber is realized; however, since the refractive index difference between adjacent modes of a multimode fiber is small (especially for higher order modes), strong coupling between modes is more likely to occur when external perturbations are applied. Multimode fibers are very sensitive to disturbances and the speckle pattern is extremely unstable. When the convolutional neural network is used for speckle image classification, a large number of speckle data samples need to be collected for training, and the precision of classification training and the convergence speed of classification are limited.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a speckle sensing system based on a multi-ring core optical fiber, which realizes multi-dimensional identification and detection of disturbance positions and disturbance directions by utilizing the characteristics of stable speckles, obvious morphological structure and different deformation of the multi-ring core optical fiber and the characteristic that the received speckle changes are different due to the fact that the disturbance in different directions is generated.
The second purpose of the invention is to provide a speckle identification method based on multi-ring core optical fiber, which only needs to train a convolution neural network on a small amount of output speckle image samples and can realize multi-dimensional detection of disturbance positions and disturbance directions.
The invention also provides a speckle recognition device based on the multi-ring core optical fiber.
A fourth object of the present invention is to provide a storage medium.
It is a fifth object of the invention to provide a computing device.
The first purpose of the invention is realized by the following technical scheme: a speckle sensing system based on multi-ring core optical fiber comprises a laser, an optical field excitation module and the multi-ring core optical fiber;
the laser is connected with the input end of the light field excitation module, the output end of the light field excitation module is connected with the head end of the multi-ring core optical fiber, the tail end of the multi-ring core optical fiber is used for being connected with the CCD camera, and speckle videos of speckle transformation when the multi-ring core optical fiber is disturbed are collected through the CCD camera.
Preferably, each ring of the multi-core optical fiber is a radial first-order annular core.
Preferably, the optical field excitation module comprises a polarizer, a first collimating mirror, a linear polarization plate, a first reflecting mirror, a spatial light modulator, a second reflecting mirror, a quarter-wave plate and a second collimating mirror which are sequentially arranged along an optical path;
the input end of the polarizer is connected with the laser through an optical fiber, the output end of the polarizer is connected with the first collimating mirror through an optical fiber, light output by the first collimating mirror is incident to the first reflector through a linear polarizer, the first reflector and the second reflector are obliquely arranged on a light path, the spatial light modulator is arranged above the first reflector and the second reflector, light emitted by the first reflector is incident to the second reflector after passing through the spatial light modulator, light emitted by the second reflector enters the quarter-wave plate and is output to the second collimating mirror through the quarter-wave plate, and the output end of the second collimating mirror is connected with one end of the multi-core optical fiber.
Preferably, the optical fiber straightening device further comprises two displacement tables which are respectively arranged at two ends of the multi-ring core optical fiber and used for fixing and straightening the multi-ring core optical fiber.
The second purpose of the invention is realized by the following technical scheme: a speckle identification method based on multi-ring core optical fiber realized by the speckle sensing system based on the first object of the invention comprises the following steps:
s1, selecting a plurality of position points on a multi-ring core optical fiber of the speckle sensing system as disturbance points;
s2, respectively applying disturbance in multiple directions of each disturbance point under the condition that the laser of the speckle sensing system is turned on;
s3, collecting speckle videos of the multi-ring core optical fiber when disturbance points corresponding to the multi-ring core optical fiber are disturbed in all directions through a CCD camera;
s4, preprocessing the speckle video to obtain a speckle image;
s5, taking the speckle image obtained after the speckle video obtained in the step S3 is preprocessed in the step S4 as a training sample, taking the disturbance point position and the disturbance direction of the disturbance force applied to the multi-ring-core optical fiber when the speckle video is obtained as a label, and training the convolutional neural network to obtain a speckle recognition model;
s6, when the disturbance position and the disturbance direction need to be identified, collecting a speckle video of the multi-ring core optical fiber under the disturbance action through a CCD camera, and preprocessing the speckle video in the step S4 to obtain a speckle image for detection;
and S7, inputting the speckle images for detection into a speckle identification model, and predicting the position and direction of the disturbed point of the multi-ring core optical fiber by the speckle identification model.
Preferably, in step S4, the specific process of preprocessing the speckle video to obtain the speckle image is as follows:
the speckle video is split into a plurality of frames, the first frame is used as a reference speckle image, all the rest frames are subjected to absolute value difference with the reference speckle image, all the speckle images after the absolute value difference are superposed, the average value of each pixel point at the corresponding position is taken as the pixel point at the corresponding position of the speckle image, and the speckle image after the averaging is obtained.
Further, step S4 includes the following steps: cutting off meaningless pixel points at the edge of the averaged speckle image, and then carrying out normalization processing to obtain a preprocessed speckle image;
in step S2, when the laser of the speckle sensing system is turned on, applying disturbance to each of the four directions of each disturbance point; the four directions are up, down, left and right directions, respectively.
The third purpose of the invention is realized by the following technical scheme: a speckle recognition device based on multi-ring core optical fiber comprises a speckle sensing system, a CCD camera and an upper computer, wherein the speckle sensing system is used for sensing speckle; the tail end of a multi-ring core optical fiber in the speckle sensing system is connected with a CCD camera, and the CCD camera is connected with an upper computer;
the CCD camera is used for acquiring a speckle video of speckle transformation when the multi-ring core optical fiber is disturbed and transmitting the acquired speckle video to the upper computer;
the upper computer is used for executing the speckle identification method of the second object of the invention.
The fourth purpose of the invention is realized by the following technical scheme: a storage medium comprising a processor and a memory for storing a program executable by the processor, wherein the processor executes the program stored in the memory to implement the speckle recognition method according to the second aspect of the present invention.
The fifth purpose of the invention is realized by the following technical scheme: a computing device stores a program that, when executed by a processor, implements the speckle recognition method according to the second object of the present invention.
Compared with the prior art, the invention has the following advantages and effects:
(1) the invention relates to a speckle sensing system based on a multi-ring core optical fiber, which comprises a laser, an optical field excitation module and the multi-ring core optical fiber; the system utilizes the characteristics of stable speckles, obvious morphological structure of the multi-ring core optical fiber and the characteristic that the received speckles change differently due to different deformations generated by the disturbances in different directions, and realizes the multidimensional identification and detection of disturbance positions and disturbance directions.
(2) In the speckle sensing system based on the multi-ring core optical fiber, the weak coupling occurs between the cores in the multi-ring core optical fiber, each ring in the ring core optical fiber is a radial first-order ring-shaped fiber core, the different mode groups have larger propagation constant difference, the weak coupling between the mode groups in each fiber core is ensured, the mode groups are kept in the grouping characteristic by designing each fiber core, and the optical fiber speckle stability and the morphological structure discrimination are ensured to be obvious.
(3) The invention relates to a speckle identification method based on multi-ring core optical fiber, which comprises the steps of selecting a plurality of position points on the multi-ring core optical fiber of a speckle sensing system as disturbance points; under the condition that a laser of the speckle sensing system is started, disturbance is respectively applied to a plurality of directions of each disturbance point; collecting speckle videos of the multi-ring core optical fiber when disturbance points corresponding to the multi-ring core optical fiber apply disturbance in all directions through a CCD camera; the speckle video is preprocessed to obtain a speckle image as follows: taking the speckle image as a training sample, taking the position and the direction of a disturbance point which exerts disturbance force on the multi-ring core optical fiber when the speckle video is obtained as a label, and training the convolutional neural network to obtain a speckle identification model; when the disturbance position and the disturbance direction need to be identified, a speckle video of the multi-ring core optical fiber under the disturbance action is collected through a CCD camera, and the speckle video is preprocessed to obtain a speckle image for detection; and inputting the speckle images for detection into a speckle identification model, and predicting by the speckle identification model to obtain the position and the direction of the disturbed point of the multi-ring core optical fiber. According to the method, the nonlinear relation between the optical fiber disturbance orientation and the speckle mode change can be established through the convolutional neural network, the characteristics of stable multi-ring core optical fiber speckles, small intra-class variance and large inter-class variance in a training set are utilized, only a small amount of output speckle image samples are required to be trained through the convolutional neural network, and the current situation that the existing multimode optical fiber utilizes the convolutional neural network to carry out speckle sensing positioning and mainly depends on a large amount of speckle sample data to carry out training is broken; meanwhile, the speckle presents different characteristics of two-dimensional space distribution by using different deformation generated by disturbing each fiber core of the multi-ring core optical fiber in different directions, and compared with a traditional one-dimensional space speckle positioning system of the multimode optical fiber, the multi-dimensional multi-mode multi-core optical fiber speckle positioning system has the characteristic of multi-dimensional detection of disturbance positions and disturbance directions.
(4) In the speckle identification method based on the multi-ring core optical fiber, the specific process of preprocessing the speckle video is as follows: the speckle video is split into a plurality of frames, the first frame is used as a reference speckle image, all the rest frames are subjected to absolute value difference with the reference speckle image, all the speckle images after the absolute value difference are superposed, and then the average value of each pixel point is taken to replace the corresponding pixel point of the speckle image, so that the speckle image after the averaging is obtained. Therefore, the method obtains the speckle image representing the disturbance position by the multi-ring core optical fiber disturbance video, can efficiently and quickly process the speckles, further accelerates the convergence speed of the convolutional neural network for sample training and improves the accuracy of classifying and identifying the speckles.
Drawings
Fig. 1 is a schematic diagram of the speckle sensing system structure of the present invention.
Fig. 2 is a schematic cross-sectional view of a multi-ring core optical fiber in the speckle sensing system of the present invention.
Fig. 3 is a schematic diagram of the pattern distribution of each ring core on the multi-ring core fiber in the speckle sensing system of the present invention.
Fig. 4 is a flow chart of the speckle identification method of the present invention.
Fig. 5 is a schematic diagram of speckle video preprocessing in the speckle identification method of the present invention.
Fig. 6 is a block diagram of the speckle recognition apparatus according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but the present invention is not limited thereto.
Example 1
The embodiment discloses a speckle sensing system based on a multi-ring core optical fiber, which comprises a laser, a light field excitation module and a multi-ring core optical fiber 2, as shown in fig. 1.
As shown in fig. 1, the laser is connected to the input end of the optical field excitation module, the output end of the optical field excitation module is connected to the head end of the multi-core optical fiber, the tail end of the multi-core optical fiber is connected to the CCD camera 3, and the speckle video of the speckle change when the multi-core optical fiber is disturbed is collected by the CCD camera 3.
In this embodiment, as shown in fig. 2, all ring-core structures in the multi-ring-core optical fiber are identical, each ring is a radial first-order ring-shaped core, and supports 6 mode group transmission, and the effective refractive index difference Δ between the mode groups iseffGreater than 10-3And weak coupling among the mode groups in each fiber core is ensured. The fiber cores are designed in such a way that the grouping characteristic is kept among the modes, and meanwhile, the fiber speckle stability and the morphological structure discrimination are ensured to be obvious. In this embodiment, to the disturbance of different optical fiber positions, the speckle variation that will cause the multi-ring core optical fiber is different to facing the disturbance of different directions, the different position fibre cores of multi-ring core optical fiber produce deformation in different degrees, thereby make the receiving speckle present different two-dimensional space distribution. Fig. 3 shows a mode distribution diagram of each ring core of the multi-ring-core optical fiber of the present embodiment.
In this embodiment, as shown in fig. 2, each core structure is uniform, and the cores are equally spaced and far apart, so as to ensure that weak coupling occurs between cores, and in this embodiment, the distance between adjacent cores may be 60 μm.
In this embodiment, as shown in fig. 1, the optical field excitation module includes a polarizer 101, a first collimating mirror 102, a linear polarization plate 103, a first reflecting mirror 104, a spatial light modulator 105, a second reflecting mirror 106, a quarter wave plate 107, and a second collimating mirror 108, which are sequentially disposed along an optical path. The input end of the polarizer is connected with the laser through an optical fiber, the output end of the polarizer is connected with the first collimating mirror through an optical fiber, light output by the first collimating mirror is incident to the first reflector through a linear polarizer, the first reflector and the second reflector are obliquely arranged on a light path, the spatial light modulator is arranged above the first reflector and the second reflector, light emitted by the first reflector is incident to the second reflector after passing through the spatial light modulator, light emitted by the second reflector enters the quarter-wave plate and is output to the second collimating mirror through the quarter-wave plate, and the output end of the second collimating mirror is connected with one end of the multi-core optical fiber.
In the system of this embodiment, two displacement stages 201 are further included, respectively disposed at two ends of the multi-core optical fiber, for fixing and straightening the multi-core optical fiber.
The system of the embodiment realizes the multidimensional identification and detection of the disturbance position and the disturbance direction by utilizing the characteristics of stable multi-ring core optical fiber speckles, obvious morphological structure and different deformation generated by the disturbance in different directions so as to lead the received speckle change to be different. For example, for a stay cable on a bridge, a multi-ring core optical fiber in a speckle sensing system is arranged in the stay cable, and when the stay cable is broken, the system can know not only the specific position of the breakage, but also the direction of the breakage based on the embodiment.
Example 2
The embodiment discloses a speckle identification method based on a multi-ring core optical fiber, which is implemented based on the speckle sensing system of the embodiment 1, and as shown in fig. 4, the method specifically comprises the following steps:
s1, selecting a plurality of position points on a multi-ring core optical fiber of the speckle sensing system as disturbance points; the positions selected as the disturbance points for the multi-core optical fiber are ((r) - (r)) as shown in fig. 1, and the distance between two adjacent disturbance points can be 0.1m in the embodiment.
And S2, respectively applying disturbance aiming at multiple directions of each disturbance point of the multi-ring-core optical fiber under the condition that the laser of the speckle sensing system is turned on. In this embodiment, the disturbance with uneven force can be generated in different disturbance points of the multi-core optical fiber and in four directions, namely, up, down, left and right directions, on each disturbance point by slightly shifting the multi-core optical fiber by hand.
And S3, collecting speckle videos of the multi-ring core optical fiber when disturbance points corresponding to the disturbance points apply disturbance in various directions through the CCD camera.
S4, preprocessing the speckle video to obtain a speckle image as follows:
splitting a speckle video into a plurality of frames, taking a first frame as a reference speckle image, carrying out absolute value difference on all the rest frames and the reference speckle image, superposing all the speckle images after the absolute value difference, taking the average value of each pixel point at the corresponding position as the pixel point at the corresponding position of the speckle image, obtaining the averaged speckle image, further cutting off the pixel points with meaningless edges of the averaged speckle image, and then carrying out normalization processing to obtain the preprocessed speckle image; as shown in particular in fig. 5.
S5, taking the speckle image obtained after the speckle video obtained in the step S3 is preprocessed in the step S4 as a training sample, taking the disturbance point position and the disturbance direction of the disturbance force applied to the multi-ring-core optical fiber when the speckle video is obtained as a label, and training the convolutional neural network to obtain a speckle recognition model;
s6, when the disturbance position and the disturbance direction need to be identified, collecting a speckle video of the multi-ring core optical fiber under the disturbance action through a CCD camera, and preprocessing the speckle video in the step S4 to obtain a speckle image for detection;
and S7, inputting the speckle images for detection into a speckle identification model, and predicting the position and direction of the disturbed point of the multi-ring core optical fiber by the speckle identification model.
In the method, the nonlinear relation between the optical fiber disturbance orientation and the speckle mode change can be established through the convolutional neural network, the characteristics of stable multi-ring core optical fiber speckles, small intra-class variance and large inter-class variance in a training set are utilized, only a small amount of output speckle image samples are required to be trained through the convolutional neural network, and the current situation that the existing multimode optical fiber utilizes the convolutional neural network to carry out speckle sensing positioning and mainly depends on a large amount of speckle sample data to carry out training is broken; meanwhile, the speckle presents different characteristics of two-dimensional space distribution by using different deformation generated by disturbing each fiber core of the multi-ring core optical fiber in different directions, and compared with a traditional one-dimensional space speckle positioning system of the multimode optical fiber, the multi-dimensional multi-mode multi-core optical fiber speckle positioning system has the characteristic of multi-dimensional detection of disturbance positions and disturbance directions.
Those skilled in the art will appreciate that all or part of the steps in the method according to the present embodiment may be implemented by a program to instruct the relevant hardware, and the corresponding program may be stored in a computer-readable storage medium. It should be noted that although the method operations of embodiment 1 are depicted in the drawings in a particular order, this does not require or imply that these operations must be performed in this particular order, or that all of the illustrated operations must be performed, to achieve desirable results. Rather, the depicted steps may change the order of execution, and some steps may be executed concurrently. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions.
Example 3
The embodiment discloses a speckle recognition device based on a multi-ring core optical fiber, which comprises a speckle sensing system, a CCD camera 3 and an upper computer 4, wherein the speckle sensing system is described in embodiment 1, as shown in FIG. 6; the tail end of a multi-ring core optical fiber in the speckle sensing system is connected with a CCD camera, and the CCD camera is connected with an upper computer; wherein:
the CCD camera is used for acquiring a speckle video of speckle transformation when the multi-ring core optical fiber is disturbed and transmitting the acquired speckle video to the upper computer;
the upper computer is used for executing the speckle identification method in the embodiment 2, and comprises the following steps:
s1, selecting a plurality of position points on a multi-ring core optical fiber of the speckle sensing system as disturbance points;
s2, respectively applying disturbance in multiple directions of each disturbance point under the condition that the laser of the speckle sensing system is turned on;
s3, collecting speckle videos of the multi-ring core optical fiber when disturbance points corresponding to the multi-ring core optical fiber are disturbed in all directions through a CCD camera;
s4, preprocessing the speckle video to obtain a speckle image as follows:
splitting a speckle video into a plurality of frames, taking a first frame as a reference speckle image, carrying out absolute value difference on all the rest frames and the reference speckle image, superposing all the speckle images after the absolute value difference, taking the average value of each pixel point at the corresponding position as the pixel point at the corresponding position of the speckle image, obtaining the averaged speckle image, further cutting off the pixel points with meaningless edges of the averaged speckle image, and then carrying out normalization processing to obtain the preprocessed speckle image;
s5, taking the speckle image obtained after the speckle video obtained in the step S3 is preprocessed in the step S4 as a training sample, taking the disturbance point position and the disturbance direction of the disturbance force applied to the multi-ring-core optical fiber when the speckle video is obtained as a label, and training the convolutional neural network to obtain a speckle recognition model;
s6, when the disturbance position and the disturbance direction need to be identified, collecting a speckle video of the multi-ring core optical fiber under the disturbance action through a CCD camera, and preprocessing the speckle video in the step S4 to obtain a speckle image for detection;
and S7, inputting the speckle images for detection into a speckle identification model, and predicting the position and direction of the disturbed point of the multi-ring core optical fiber by the speckle identification model.
Example 4
The embodiment discloses a storage medium, which includes a processor and a memory for storing a program executable by the processor, and is characterized in that when the processor executes the program stored by the memory, the speckle recognition method described in embodiment 2 is implemented as follows:
s1, selecting a plurality of position points on a multi-ring core optical fiber of the speckle sensing system as disturbance points;
s2, respectively applying disturbance in multiple directions of each disturbance point under the condition that the laser of the speckle sensing system is turned on;
s3, collecting speckle videos of the multi-ring core optical fiber when disturbance points corresponding to the multi-ring core optical fiber are disturbed in all directions through a CCD camera;
s4, preprocessing the speckle video to obtain a speckle image as follows:
splitting a speckle video into a plurality of frames, taking a first frame as a reference speckle image, carrying out absolute value difference on all the rest frames and the reference speckle image, superposing all the speckle images after the absolute value difference, taking the average value of each pixel point at the corresponding position as the pixel point at the corresponding position of the speckle image, obtaining the averaged speckle image, further cutting off the pixel points with meaningless edges of the averaged speckle image, and then carrying out normalization processing to obtain the preprocessed speckle image;
s5, taking the speckle image obtained after the speckle video obtained in the step S3 is preprocessed in the step S4 as a training sample, taking the disturbance point position and the disturbance direction of the disturbance force applied to the multi-ring-core optical fiber when the speckle video is obtained as a label, and training the convolutional neural network to obtain a speckle recognition model;
s6, when the disturbance position and the disturbance direction need to be identified, collecting a speckle video of the multi-ring core optical fiber under the disturbance action through a CCD camera, and preprocessing the speckle video in the step S4 to obtain a speckle image for detection;
and S7, inputting the speckle images for detection into a speckle identification model, and predicting the position and direction of the disturbed point of the multi-ring core optical fiber by the speckle identification model.
In this embodiment, the storage medium may be a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a Random Access Memory (RAM), a usb disk, a removable hard disk, or other media.
Example 5
The present embodiment discloses a computing device, which stores a program that, when executed by a processor, implements the speckle recognition method described in embodiment 2, as follows:
s1, selecting a plurality of position points on a multi-ring core optical fiber of the speckle sensing system as disturbance points;
s2, respectively applying disturbance in multiple directions of each disturbance point under the condition that the laser of the speckle sensing system is turned on;
s3, collecting speckle videos of the multi-ring core optical fiber when disturbance points corresponding to the multi-ring core optical fiber are disturbed in all directions through a CCD camera;
s4, preprocessing the speckle video to obtain a speckle image as follows:
splitting a speckle video into a plurality of frames, taking a first frame as a reference speckle image, carrying out absolute value difference on all the rest frames and the reference speckle image, superposing all the speckle images after the absolute value difference, taking the average value of each pixel point at the corresponding position as the pixel point at the corresponding position of the speckle image, obtaining the averaged speckle image, further cutting off the pixel points with meaningless edges of the averaged speckle image, and then carrying out normalization processing to obtain the preprocessed speckle image;
s5, taking the speckle image obtained after the speckle video obtained in the step S3 is preprocessed in the step S4 as a training sample, taking the disturbance point position and the disturbance direction of the disturbance force applied to the multi-ring-core optical fiber when the speckle video is obtained as a label, and training the convolutional neural network to obtain a speckle recognition model;
s6, when the disturbance position and the disturbance direction need to be identified, collecting a speckle video of the multi-ring core optical fiber under the disturbance action through a CCD camera, and preprocessing the speckle video in the step S4 to obtain a speckle image for detection;
and S7, inputting the speckle images for detection into a speckle identification model, and predicting the position and direction of the disturbed point of the multi-ring core optical fiber by the speckle identification model.
In this embodiment, the computing device may be a desktop computer, a notebook computer, a smart phone, a PDA handheld terminal, a tablet computer, or other terminal devices.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
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