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Method and system for detecting light spot track of single-fiber scanner based on deep learning

A deep learning and detection method technology, applied in the field of optical scanning, can solve the problems of low anti-interference ability and low precision, and achieve the effect of improving accuracy and anti-interference ability and solving distortion

Pending Publication Date: 2021-10-26
CHINA JILIANG UNIV
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Problems solved by technology

[0003] The present invention proposes a method and system for spot track detection of a single fiber scanner based on deep learning, mainly to realize the real-time position detection of the above spot track, and to solve the problem of low anti-interference ability and precision of the system

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  • Method and system for detecting light spot track of single-fiber scanner based on deep learning
  • Method and system for detecting light spot track of single-fiber scanner based on deep learning
  • Method and system for detecting light spot track of single-fiber scanner based on deep learning

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Embodiment Construction

[0039] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments, wherein the schematic embodiments and descriptions are only used to explain the present invention, but not as improper limitations to the present invention.

[0040] like figure 1 As shown, the present invention proposes a single fiber scanner spot track detection method based on deep learning, the method comprising:

[0041] S1. Collect raw data, collect N groups of scanning feedback signal sequences through the single fiber scanner position sensor, obtain N groups of temperature signal sequences through the temperature sensor, and obtain N groups of light spot scanning trajectory signal sequences through the position sensitive detector.

[0042] S2. Make a data set, make the signal sequence obtained in step 1 and the known optical fiber scanning driving signal into a training data set, wherein the feedback signal sequence, driving signal sequen...

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Abstract

The invention provides a method and system for detecting a light spot track of a single-fiber scanner based on deep learning, and the method comprises the steps: collecting original data, making a data set, training a model, and using the model, and inputting the original data collected in real time into the model trained by a network to obtain light spot scanning track prediction data. Correspondingly, the invention further provides a system for detecting the light spot track of the single-fiber scanner based on deep learning so as to realize the method. According to the invention, real-time detection of the light spot track of the single-fiber scanner is realized, the problem of distortion of an image shot or projected by the single-fiber scanner is solved, and the precision and the anti-interference capability of the system are improved.

Description

technical field [0001] The present invention relates to the field of optical scanning technology, in particular to a method and system for detecting a spot track of a single optical fiber scanner based on deep learning, which can be used in the fields of medical imaging and photoelectric detection. Background technique [0002] A single fiber scanner is a device that drives a single fiber to swing through an actuator, so that the light spot emitted from the fiber cantilever scans a certain range on the target. It can be used for endoscopes, micro-projection and three-dimensional scanning detection, etc. field. Among them, how to obtain the spot motion track in real time and accurately is the difficulty and focus in the single-fiber scanning technology. At present, the mature control scheme in the single-fiber scanning technology adopts the research report (High Performance Open Loop Control of Scanning with a Small Cylindrical Cantilever Beam , Journal of Sound and Vibratio...

Claims

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Application Information

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IPC IPC(8): G06N3/04G06N3/08G02B26/10G01M11/02G06T5/00
CPCG06N3/08G02B26/103G01M11/0242G06N3/045G06T5/80
Inventor 李裔王旭杜瀚文章慧英孟彦龙
Owner CHINA JILIANG UNIV
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