Lens Apparatus Adaptive Control via Machine Learning
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Solution Overview
Problem
Existing image pickup apparatuses face challenges in achieving optimal performance in terms of positioning accuracy, driving speed, power consumption, and quietness, as these factors are interdependent and situation-dependent, requiring adaptable operation modes for various imaging scenarios and operator preferences.
Innovation Solution
A lens apparatus incorporating a driving device, detector, and processor with a machine learning model that generates control signals based on detected states and apparatus information, allowing for adaptive optimization of driving performance through a neural network algorithm and reward-based training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If high-speed automatic focusing and zooming operations are performed for quick still-image capturing, then productivity is improved, but operation noise increases and quietness deteriorates
Solution Approach 1:
The system dynamically switches between high-speed operation mode for still images and silent operation mode for moving images based on the capture mode detected by the detector. The driving device adjusts its operating characteristics in real-time according to the detected state, enabling fast focusing when needed and quiet operation when required.
Solution Approach 2:
The driving device changes its operational parameters (speed, acceleration, noise level) based on the detected capture mode. In silent mode, the driving speed and acceleration are limited to reduce noise, while in high-speed mode, the parameters are optimized for rapid focusing and zooming operations.
2Object-affected harmful factors
If driving speed and acceleration are limited to reduce operation noise, then quietness is improved, but positioning accuracy and control followability deteriorate
Solution Approach 1:
The system dynamically adjusts driving speed and acceleration limits based on the detected capture mode. During moving image capturing in silent mode, speed and acceleration are limited to reduce noise. During still-image capturing in high-speed mode, the limits are removed or reduced to enable fast and accurate focusing.
Solution Approach 2:
The driving device changes its speed and acceleration parameters based on operational context. The processor receives detected state information and adjusts the driving parameters accordingly, allowing the system to optimize both noise reduction and positioning accuracy for different imaging situations.
3Productivity
If high-speed operations are performed continuously, then productivity is improved, but power consumption increases
Solution Approach 1:
The system dynamically adjusts power consumption based on the detected capture mode and operational requirements. During moving image capturing, the system operates in a more energy-efficient silent mode with reduced driving speeds. During still-image capturing, high-speed operations consume more power but are necessary for productivity.
Data Source
AI summary
A lens apparatus includes an optical member, a driving device configured to perform driving of the optical member, a detector configured to detect a state related to the driving, and a processor configured to generate a control signal for the driving device based on first information about the detected state, wherein the processor includes a machine learning model configured to generate an output related to the control signal based on the first information and second information about the lens apparatus, and is configured to output the first information and the second information to a generator configured to perform generation of the machine learning model.