Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Automatic focusing method and system based on convolutional neural network

A convolutional neural network and automatic focusing technology, applied in the field of automatic focusing methods and systems based on convolutional neural networks, can solve problems such as narrow applicability, limited engineering applications, and difficulty in extracting artificial defocus features. Avoid inaccurate measurement and simple focusing process

Active Publication Date: 2021-07-13
CHANGCHUN INST OF OPTICS FINE MECHANICS & PHYSICS CHINESE ACAD OF SCI
View PDF9 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that additional auxiliary mechanisms such as distance measuring devices need to be added, which increases the complexity and structural cost of the system.
This type of method is more suitable for continuous automatic focusing occasions where the object distance changes continuously, but there is a problem of difficulty in extracting artificial defocus features, which limits the engineering application of this method
Chinese patent CN106249508B proposes an automatic focusing method, which first extracts face blocks in the field of view, and inversely deduces the object distance information according to the proportional relationship between the face block area and the actual face area, and then according to the conjugate relationship of the object image , to adjust the optical parameters to achieve automatic focusing. The method described in this patent is fast and effective, but it is only suitable for occasions where there are human faces in the field of view, and its applicability is narrow

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Automatic focusing method and system based on convolutional neural network
  • Automatic focusing method and system based on convolutional neural network
  • Automatic focusing method and system based on convolutional neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0035] The technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0036] figure 1 It is a schematic flowchart of an automatic focusing method based on a convolutional neural network in an embodiment of the present invention. Such as figure 1 As shown, the automatic focusing method based on convolutional neural network in this embodiment includes the following steps:

[0037] Step 1 (S100): establishing a defocused image data set;

[0038] Step 2 (S200): building a defocus estimation model, and using the defocus image data set to train the defocus estimation model;

[0039] Step 3 (S300): Obtain the current frame image collected by the image detector, and input the current frame image to the trained defocus estimation model to predict the image defocus amount, and obtain the defocus amount relative to the current focus motor position normalized output value;

[0040] Step 4 (...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention relates to an automatic focusing method and system based on a convolutional neural network, wherein the method includes the following steps: establishing a defocus image data set; building a defocus estimation model, and using the defocus image data set to estimate the defocus amount The model is trained; the current frame image collected by the image detector is obtained, and the current frame image is input to the trained defocus estimation model to predict the image defocus amount, and the defocus amount relative to the current focus motor position is normalized Calculate the defocus value according to the normalized output value of the defocus amount, and judge the adjustment direction of the focus motor; adjust the position of the focus motor according to the defocus value and adjustment direction, and complete the image detector. Auto focus. The invention can automatically extract the defocus amount of the input image by using the trained defocus amount estimation model, effectively avoiding the problem that the artificial defocus amount feature is difficult to extract, the focusing process is simple and fast, and can be used for continuous changes in object distance Continuous focusing occasions.

Description

technical field [0001] The invention relates to the field of imaging technology, in particular to an automatic focusing method and system based on a convolutional neural network. Background technique [0002] Clear imaging is an important prerequisite for the normal operation of imaging equipment, and inability to focus accurately is the primary reason for affecting imaging clarity. In order to quickly and clearly image the targets in different positions and different moving states, the optical system must realize real-time and fast automatic focusing. [0003] Autofocus methods for imaging devices can be divided into two categories: ranging methods and image methods. [0004] The ranging method mainly uses ranging devices such as laser rangefinders to measure the distance of the observed target (ie, the object distance), and adjusts the optical parameters according to the conjugate relationship of the object and image to achieve fast focusing of the system. The disadvanta...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): H04N5/232G06N3/04G06N3/08
CPCG06N3/08H04N23/67H04N23/64G06N3/045
Inventor 张艳超余毅高策唐伯浩赵立荣
Owner CHANGCHUN INST OF OPTICS FINE MECHANICS & PHYSICS CHINESE ACAD OF SCI
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products