Rare Instance Classification for Autonomous Vehicle Object Recognition
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately classifying rarely occurring objects, leading to repeated incorrect classifications, which can impact navigation and decision-making.
Innovation Solution
A dual-classifier system comprising a common instance classifier and a rare instance classifier, where the rare instance classifier computes a rarity score to adjust the classification output of the common instance classifier, reducing misclassification likelihoods by applying weights to object scores based on the rarity score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a common instance classifier is used to classify all objects, then the system is simple and computationally efficient, but rarely occurring objects are frequently misclassified
Solution Approach 1:
The classification system is segmented into two specialized classifiers: a common instance classifier for frequently occurring objects and a rare instance classifier for rarely occurring objects. Each classifier is optimized for its specific domain, with the rare instance classifier receiving higher-weighted inputs for rare object detection. This segmentation resolves the contradiction by improving rare object classification accuracy without requiring a complete redesign of the entire system.
Solution Approach 2:
The system dynamically changes the weighting parameter of classifier outputs based on object rarity. When a rare object is detected, the rare instance classifier's output weight is increased relative to the common instance classifier. This parameter adjustment allows the system to adaptively improve classification reliability for rare objects while maintaining the simplicity of the dual-classifier structure.
2Reliability
If the common instance classifier is re-trained to improve rare object classification, then classification accuracy improves, but computational resources and time are consumed
Solution Approach 1:
A rare instance classifier is pre-trained and maintained separately from the common instance classifier. This preliminary preparation ensures that when rare objects need to be classified, the system can immediately utilize the pre-trained rare instance classifier without requiring re-training of the entire system. The rare instance classifier is updated independently using rare object training data, avoiding the time loss associated with re-training the common classifier.
Solution Approach 2:
The rare instance classifier acts as an intermediary component that handles rare object classification independently. Instead of modifying the main common instance classifier, the system introduces this intermediate classifier that specializes in rare objects. The final classification decision is made by combining outputs from both classifiers with adaptive weighting, thus improving rare object accuracy without re-training the primary classifier.
3Reliability
If a dual-classifier system is implemented to reduce misclassification, then classification accuracy improves, but computational resources increase
Solution Approach 1:
The system applies partial action by selectively activating the rare instance classifier based on detected object rarity. For common objects, only the efficient common instance classifier processes the input. For rare objects, the system activates both classifiers with adaptive weighting toward the rare instance classifier. This partial activation strategy improves overall classification accuracy while minimizing unnecessary computational energy consumption for common cases.
Solution Approach 2:
The system dynamically changes the weighting parameter between the two classifiers based on the detected object's rarity score. When rare objects are detected, the rare instance classifier's weight is increased; for common objects, the common instance classifier's weight dominates. This parameter adaptation allows the system to optimize the balance between classification reliability and computational energy usage in real-time.
Data Source
AI summary
In some implementations, an image classification system of an autonomous or semi-autonomous vehicle is capable of improving multi-object classification by reducing repeated incorrect classification of objects that are considered rarely occurring objects. The system can include a common instance classifier that is trained to identify and recognize general objects (e.g., commonly occurring objects and rarely occurring objects) as belonging to specified object categories, and a rare instance classifier that is trained to compute one or more rarity scores representing likelihoods that an input image is correctly classified by the common instance classifier. The output of the rare instance classifier can be used to adjust the classification output of the common instance classifier such that the likelihood of input images being incorrectly classified is reduced.