Image Recognition Assistance Apparatus for Low-Light Clarity
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Solution Overview
Problem
The recognition accuracy of image recognition engines is limited by image quality, particularly affected by brightness and shutter speed, leading to reduced performance in capturing images with low illuminance or excessive motion blur and noise.
Innovation Solution
An image recognition assistance apparatus that adjusts image quality parameters, such as shutter speed and luminance, based on recognition results to optimize image quality for improved recognition accuracy, using a feedback loop to repeatedly adjust settings until stable high recognition results are achieved.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the shutter speed is increased to reduce motion blur, then image clarity is improved, but the image brightness decreases leading to increased noise
Solution Approach 1:
The patent implements a feedback mechanism where recognition results are acquired and used to determine optimal set values for image output unit parameters. The system repeatedly adjusts shutter speed and luminance based on recognition accuracy feedback until stable high recognition results are achieved, resolving the trade-off between motion blur reduction and noise increase.
Solution Approach 2:
The patent dynamically changes parameters (shutter speed and luminance) of the image output unit based on recognition results. By adjusting these parameters in response to recognition accuracy feedback, the system optimizes the balance between reducing motion blur and controlling noise levels.
2Measurement precision
If the luminance is increased to improve image brightness, then recognition accuracy is improved, but the noise in the image increases
Solution Approach 1:
The system uses recognition results as feedback to determine optimal luminance settings. By repeatedly adjusting luminance based on recognition accuracy feedback, the system finds the optimal balance between improving recognition accuracy and controlling noise, rather than simply maximizing brightness.
Solution Approach 2:
The patent dynamically adjusts the luminance parameter of the image output unit based on recognition results. This parameter change approach allows the system to optimize luminance levels for each specific recognition task, balancing brightness improvement against noise increase.
3Measurement precision
If additional learning processes are implemented to improve recognition accuracy, then recognition performance is improved, but the system complexity and processing time increase
Solution Approach 1:
Instead of implementing additional learning processes, the patent uses a feedback loop where recognition results are acquired and used to determine optimal set values for the image output unit. This feedback-based parameter optimization achieves improved recognition accuracy without increasing system complexity or requiring additional learning training processes.
Data Source
AI summary
An image recognition assistance apparatus according to the present disclosure includes: a recognition result acquisition unit configured to acquire a recognition result of image recognition carried out by an image recognition engine on a target image output by an image output unit using a predetermined set value; and a setting unit configured to determine a set value with which the recognition result meets a predetermined criterion and set the determined set value in an image output unit. Accordingly, by adjusting a target image to be input to the image recognition engine in consideration of the recognition results obtained by the image recognition engine, improvement of a recognition accuracy is assisted.