Object Descriptor Tokens for Long-Range Spoof-Resistant Detection
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Solution Overview
Problem
Existing object detection technologies struggle to accurately differentiate between real and spoof objects, particularly in long-range scenarios, and are hindered by limited sensor resolution, adverse weather conditions, and occlusions, leading to potential safety hazards in autonomous driving systems.
Innovation Solution
Utilizing a trained transformer encoder to generate object descriptor tokens that classify real objects, including long-range objects, by integrating a sensor-specific knowledge database with neural radiance fields for enhanced 3D reconstruction and self-attention mechanisms to improve object detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional object detection pipelines are used, then processing speed is maintained, but detection accuracy for real objects and ability to avoid spoof objects deteriorates
Solution Approach 1:
The patent introduces object descriptor tokens as an intermediary component between the standard object detection pipeline and the final classification stage. These tokens serve as a mediator that captures additional semantic information about objects, enabling more accurate differentiation between real and spoof objects without completely redesigning the detection pipeline. The descriptor tokens are generated by a transformer encoder that processes object features and produces enhanced representations that can be integrated with existing detection algorithms.
Solution Approach 2:
The patent segments the object detection process into distinct functional components: standard object token generation, descriptor token generation through transformer encoding, and fused classification. By separating the descriptor extraction function from the main detection pipeline, the system can improve accuracy for specific tasks (real vs. spoof classification) while maintaining the efficiency of the overall pipeline. This segmentation allows independent optimization of different detection aspects.
2Measurement precision
If sensor resolution is increased to improve long-range detection, then detection capability for distant objects improves, but system cost and complexity increase
Solution Approach 1:
The patent replaces physical sensor upgrades with a computational approach using transformer encoders and object descriptor tokens. Instead of increasing sensor resolution hardware complexity to improve long-range detection, the system uses neural network-based descriptor generation to enhance the informational content of existing sensor data. The transformer encoder processes object features to create rich descriptors that compensate for limited sensor resolution, particularly for distant objects where physical sensor improvements would be costly and complex.
3Measurement precision
If more feature data is collected to improve object classification, then classification accuracy improves, but data processing time and computational load increase
Solution Approach 1:
The patent applies partial action by generating descriptor tokens only for detected objects rather than processing all image data through the transformer encoder. The system selectively applies the computationally intensive descriptor generation process only to objects that have been initially detected by the standard pipeline, avoiding unnecessary processing of background and non-object regions. This selective approach maintains high classification accuracy for relevant objects while reducing overall computational load and processing time.
Data Source
AI summary
A device for object detection includes one or more memories configured to store image data; and processing circuitry connected to the one or more memories, the processing circuitry configured to: generate bird's-eye-view (BEV) object feature data from the image data, including BEV object tokens, the BEV object tokens being indicative of a first set of information used for object detection in the image data; generate an input for a transformer encoder based on at least some of the BEV object feature data or the image data; generate object descriptor tokens based on applying the transformer encoder to the input, the object description tokens being indicative of a second set of information used for object detection in the image data, the second set of information being usable for classifying real objects; and output object detection information based on the BEV object tokens and the object descriptor tokens.