Semantic Feature Perception Error Correction
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Solution Overview
Problem
Current perception systems for object recognition and tracking rely heavily on appearance-based information, lacking conceptual understanding and context, leading to issues like false positives, missed detections, and increased size, weight, and power (SWAP) due to the need for sensor redundancy.
Innovation Solution
A perception system that generates semantic features using processor-executed instructions to detect and correct errors by creating object and background embeddings, determining probabilistic distributions, and adjusting perception parameters based on cosine similarity metrics and conditional random fields, reducing the need for redundancy and improving detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor and sensor modality redundancy is employed to improve perception performance, then reliability is improved, but size, weight, and power increase
Solution Approach 1:
The patent introduces semantic features as an intermediary layer between raw sensor data and object detection results. These semantic features encode contextual information about object relationships, backgrounds, and scene semantics, allowing the system to improve reliability through intelligent processing rather than hardware redundancy
Solution Approach 2:
The patent replaces the mechanical approach of adding more sensors (hardware redundancy) with an information-processing approach using semantic features and machine understanding. This substitution uses computational methods to achieve reliability improvements without increasing physical system size
2Speed
If appearance-based information is used for object recognition, then detection speed is maintained, but conceptual understanding and context awareness are lacking
Solution Approach 1:
The patent adds a semantic dimension to traditional appearance-based detection. By incorporating semantic features that represent conceptual relationships, object backgrounds, and scene context, the system transitions from purely visual appearance matching to multi-dimensional understanding that includes both appearance and meaning
Solution Approach 2:
The patent creates a composite information representation that combines appearance-based features with semantic features. This composite approach integrates both visual data and conceptual understanding, allowing the system to maintain detection speed while gaining contextual awareness
3Measurement precision
If deep-learning models are used for perception, then detection accuracy is improved, but system interpretability and error analysis become difficult
Solution Approach 1:
The patent implements feedback mechanisms where semantic features provide interpretable intermediate representations that can be analyzed to understand detection decisions. The semantic information about object relationships and backgrounds serves as feedback that makes the black-box deep learning process more transparent and analyzable
Solution Approach 2:
The patent introduces semantic features as an intermediary layer between raw sensor data and final detection results. This intermediary representation is both machine-processable for maintaining accuracy and human-interpretable for improving system understandability and error analysis
Data Source
AI summary
Described is a system for detecting and correcting perception errors in a perception system. In operation, the system generates a list of detected objects from perception data of a scene, which allows for the generation of a list of background classes from backgrounds in the perception data associated with the list of detected objects. For each detected object in the list of detected objects, a closest background class is identified from the list of background classes. Vectors can then be used to determine a semantic feature, which is used to identify axioms. An optimal perception parameter is then generated, which is used to adjust perception parameters in the perception system to minimize perception errors.