Classifier Training Using Multi-Perspective Data Expansion
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Solution Overview
Problem
Existing object detection classifiers in AI systems are limited to specific perspectives and applications, requiring retraining with new data for different viewpoints or scenarios, which is inefficient and restricts their adaptability.
Innovation Solution
A method that uses multiple image recording devices from varying perspectives to expand the detection capability of an AI module by creating a training data set that includes images and labels from different viewpoints, allowing the classifier to detect objects from new angles without extensive retraining, and enables the generation of 3D models for flexible perspective adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a classifier is trained using training data from a specific perspective, then detection accuracy for that perspective is improved, but adaptability to new perspectives deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-training the classifier with training data from multiple perspectives before actual object detection is needed. This allows the classifier to develop robust detection capabilities across various viewpoints in advance, eliminating the need for retraining when encountering new perspectives during operation.
Solution Approach 2:
The patent implements universality by designing a classifier that can detect objects from multiple different perspectives simultaneously. By using training data encompassing various viewpoints, the single classifier becomes multi-functional, capable of handling diverse detection scenarios without requiring separate specialized classifiers for each perspective.
2Adaptability or versatility
If new training data is produced for new applications, then detection capability for new applications is improved, but time and computational resources are consumed
Solution Approach 1:
The patent applies preliminary action by pre-training the classifier with training data from multiple perspectives before actual object detection is needed. This allows the classifier to develop robust detection capabilities across various viewpoints in advance, eliminating the need for retraining when encountering new perspectives during operation.
Data Source
AI summary
A method and a device for improved training of a classifier. For this purpose, the device for detecting an object has a first image recording device, which is situated in a first position for recording the object from a first perspective. The device furthermore has a stored first classifier, which is configured to detect the object based on the recorded first perspective. At least one second image recording device is situated in a second position for recording the object from a second perspective, which differs from the first perspective. Furthermore, a data processing device is configured to detect the object recorded by the first image recording device based on the first classifier and to assign to the second perspective a class predetermined by the first classifier. This makes it possible to expand the existing classifier to include the detection of the object from different additional perspectives.