Image Recognition Model Training for Quality Consistency
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Solution Overview
Problem
Conventional neural networks trained with high-quality images perform poorly on low-quality images, and separate networks for high-quality and low-quality images are inefficient, lacking consistency in recognition performance across varying image qualities.
Innovation Solution
A two-stage training method is employed, where a primary recognition model is trained with high-quality images to extract features and reconstruct high-quality images, and a secondary model is trained with both high-quality and low-quality images, with fixed decoder parameters to enhance feature extraction and image reconstruction, ensuring consistent recognition performance regardless of image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a neural network is trained with high-quality images, then recognition performance on high-quality images is improved, but recognition performance on low-quality images deteriorates
Solution Approach 1:
The patent applies universality by designing a single image recognition model that functions effectively across multiple image quality conditions. The model is trained with a composite loss function that combines recognition loss and reconstruction loss, enabling it to simultaneously optimize for both high-quality and low-quality image recognition tasks, thus achieving multi-functional performance consistency.
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting the weighting coefficients of different loss functions during training. The recognition loss and reconstruction loss are combined with adjustable weights that can be optimized to balance the competing objectives of maintaining high recognition accuracy while ensuring robust performance across varying image qualities.
2Measurement precision
If separate neural networks are used for high-quality and low-quality images, then recognition performance for each quality level is improved, but device complexity increases
Solution Approach 1:
The patent applies merging by consolidating multiple recognition tasks into a single unified model. Instead of maintaining separate neural networks for high-quality and low-quality images, the invention integrates both recognition objectives into one model with a composite loss function, thereby reducing system complexity while maintaining specialized performance for different image qualities.
3Measurement precision
If image quality is improved through preprocessing, then recognition performance is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies self-service by enabling the image recognition model to automatically adapt to varying image qualities without requiring external preprocessing interventions. The model internally handles quality variations through its training on diverse image qualities and the use of reconstruction loss, which teaches the model to extract meaningful features regardless of input quality, thereby eliminating the need for time-consuming preprocessing steps.
Data Source
AI summary
An image recognition method includes: receiving an input image of a first quality; extracting an input feature of a second quality of the input image from the input image by inputting the input image to an encoding model in an image recognizing model; and generating a recognition result for the input image based on the input feature.