Object Recognition Model Adaptation via Dynamic Background Updates
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Solution Overview
Problem
Existing object recognition technologies face challenges in maintaining accurate object recognition in varying shooting environments, such as changes in sunlight or device installation positions, due to differences in background brightness and object placement, leading to reduced recognition accuracy.
Innovation Solution
An object recognition system that includes a camera and processor, which captures and updates background images for training, allowing the machine learning model to adapt to environmental changes by re-acquiring background images when necessary, ensuring accurate object recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a machine learning model is trained with composite images generated by CG technology, then the model is less affected by environmental changes, but there is a limit on generation of virtual images for training when the actual environment changes significantly
Solution Approach 1:
The patent implements dynamic model updates by periodically re-acquiring background images and re-training the machine learning model when environmental changes are detected. This transforms the static training process into a dynamic one that adapts to changing conditions, resolving the contradiction between reliability and adaptability.
Solution Approach 2:
The system incorporates feedback mechanisms by detecting environmental changes (such as brightness variations) and triggering model re-training based on actual performance needs. This feedback loop allows the model to maintain high recognition accuracy while adapting to new environments.
2Reliability
If the machine learning model is updated frequently to adapt to environmental changes, then recognition accuracy is maintained, but the complexity and computational cost of the system increases
Solution Approach 1:
The patent employs periodic action by updating the model at predetermined time intervals or when specific environmental change thresholds are met, rather than continuously. This reduces unnecessary computational overhead while maintaining recognition accuracy, balancing reliability with system complexity.
Data Source
AI summary
Provided is a method for performing accurate object recognition in a stable manner in consideration of changes in a shooting environment. In such a method, a camera captures an image of a shooting location where an object is to be placed and an object included in an image of the shooting location is recognized utilizing a machine learning model for object recognition. The method further involves: determining necessity of an update operation on the machine learning model for object recognition at a predetermined time; when the update operation is necessary, causing the camera to capture an image of the shooting location where no object is placed to thereby re-acquire a background image for training; and causing the machine learning model to be trained using a composite image of a backgroundless object image and the re-acquired background image for training as training data.