Camera System With Spectroscopy Mirror For Machine Learning Data
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Solution Overview
Problem
It is challenging to obtain diverse types of captured image data while matching various conditions, which limits the performance of machine learning models, as existing methods often rely on similar imaging environments or artificially created data.
Innovation Solution
A camera system with a configuration that includes an imaging optical system, a mirror for spectroscopy, and two imaging elements that output different types of captured image data, such as color and distance image data, by sharing the same imaging optical system and synchronizing their operations to capture data under similar conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a plurality of types of captured image data are obtained in a similar imaging environment, then the device complexity is reduced and ease of manufacture is improved, but the measurement precision and reliability of the learning data are insufficient
Solution Approach 1:
The imaging system is segmented into multiple imaging elements (first imaging element for color image data, second imaging element for distance image data) that capture different types of data simultaneously through the same optical system, enabling diverse learning data acquisition without compromising precision
Solution Approach 2:
Multiple imaging elements are merged to share a common optical system (lens, aperture, imaging optical system), allowing different types of captured image data to be obtained under identical imaging conditions while reducing device complexity and manufacturing difficulty
2Device complexity
If captured image data are artificially created or obtained from single-source data, then the device complexity is reduced, but the reliability and performance of the learning model deteriorates
Solution Approach 1:
A light separating member (mirror or prism) acts as an intermediary that divides the light from the optical system into multiple paths directed to different imaging elements, enabling simultaneous capture of multiple data types from the same scene without artificial creation
Solution Approach 2:
The optical system is designed with universal functionality to serve multiple imaging elements simultaneously, where a single optical path can be distributed to capture both color and distance image data, improving reliability while maintaining simplicity
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This configuration allows for the generation of high-quality learning data sets that can improve the accuracy of machine learning models by ensuring that different types of image data are captured under the same conditions, enabling better inference and algorithm development.
Implementation Method 1
a mirror configured to perform spectroscopy on light incident via the imaging optical system
Implementation Method 2
a first imaging element configured to output first captured image data used as learning data of machine learning by receiving incident light incident via the mirror and performing photoelectric conversion
Data Source
AI summary
A camera system includes: an imaging optical system including an optical element for imaging; a mirror configured to perform spectroscopy on light incident via the imaging optical system; a first imaging element configured to output first captured image data used as learning data of machine learning by receiving the incident light incident via the mirror and performing photoelectric conversion; and a second imaging element configured to output second captured image data used as the learning data by receiving the incident light incident via the mirror and performing photoelectric conversion.