Dynamic Model Weight Adaptation for Image Recognition
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Solution Overview
Problem
Image-based object recognition technologies face challenges in maintaining a high recognition rate due to overfitting caused by fixed model weights that do not adapt to changes in environment variables such as weather, camera angles, and target environment features, making commercialization difficult in industrial settings.
Innovation Solution
An image-based object recognition method and system that performs primary and secondary learning, where environment variables are incorporated by adjusting weights based on the accuracy of primary reasoning results, allowing for dynamic adaptation and improved recognition rates without additional data labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If fixed model weights are used for object recognition, then the model structure remains simple and stable, but the recognition rate decreases when environment variables change
Solution Approach 1:
The patent implements dynamic weight adjustment by introducing an environment variable detection mechanism that automatically modifies model weights based on current environmental conditions. The system transitions from static fixed weights to dynamic adaptive weights, allowing the model to respond to changes in weather, lighting, and other environmental factors while maintaining operational stability through controlled adaptation.
Solution Approach 2:
The patent changes the parameter state of model weights from fixed to variable by introducing environment-based weight modification. The system detects environmental variables and adjusts weight parameters accordingly, transforming the model from a rigid structure to one that can adapt its parameters to match environmental conditions, thereby maintaining high recognition rates across varying contexts.
2Reliability
If the model adapts to different environment variables, then the object recognition rate improves, but the system complexity increases
Solution Approach 1:
The patent segments the adaptation mechanism into distinct functional modules: environment variable detection, weight adjustment calculation, and model parameter modification. This segmentation allows each component to be independently optimized and managed, reducing overall system complexity while enabling sophisticated adaptive behavior through coordinated module interaction.
Solution Approach 2:
The system implements self-service adaptation by automatically detecting environmental variables and adjusting its own weights without external intervention. The model performs self-diagnosis of environmental conditions and self-adjustment of parameters, eliminating the need for complex external control systems and manual recalibration, thereby improving recognition rates while keeping system complexity manageable.
3Reliability
If re-learning is performed to incorporate environment variables, then the recognition performance improves, but additional data labeling is required
Solution Approach 1:
The patent enables the system to perform its own adaptation by using environment variable detection to directly adjust weights without requiring external data labeling. The model serves itself by automatically identifying environmental conditions and modifying its parameters accordingly, eliminating the time-consuming data collection and labeling process while maintaining improved recognition performance.
Solution Approach 2:
Instead of changing the training data parameters through additional labeling, the patent directly modifies model weight parameters based on detected environment variables. This approach achieves performance improvement through parameter adaptation rather than data re-collection, avoiding the time loss associated with additional data labeling while still incorporating environmental context into the recognition process.
Data Source
AI summary
Disclosed herein are image-based object recognition method and system by and in which a learning server performs image-based object recognition based on the learning of environment variable data. The image-based object recognition method includes: receiving an image acquired through at least one camera, and segmenting the image on a per-frame basis; primarily learning labeling results for one or more objects in the image segmented on a per-frame basis; performing primary reasoning by performing object detection in the image through a model obtained as a result of the primary learning; performing data labeling based on the results of the primary reasoning, and performing secondary learning with weights allocated to respective boxes obtained by the primary reasoning and estimated as object regions; and finally detecting one or more objects in the image through a model generated as a result of the secondary learning.