Transformer Street Sign Detection Parallel Recognition
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Solution Overview
Problem
Existing systems for detecting and recognizing street signs in a motor vehicle environment face challenges due to non-standardized signs containing extensive text and varied shapes, requiring advanced Natural Language Understanding (NLU) and efficient processing to support semi-autonomous or fully autonomous vehicle operations.
Innovation Solution
A method utilizing a Multi-Task-Learning (MTL) approach with a transformer-based architecture for joint detection and recognition of street signs, incorporating self-attention mechanisms and positional embeddings to process both spatial and sequential features, enabling parallel processing for improved efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pipeline-based methods with separate detection and recognition models are used, then detection and recognition can be performed, but processing time and memory resources increase
Solution Approach 1:
The patent merges the detection model and recognition model into a single unified model that performs both object detection and text recognition simultaneously. This integration eliminates the sequential pipeline approach where separate models process data in stages, thereby reducing overall processing time and memory requirements while maintaining detection and recognition accuracy through a coordinated multi-task architecture
Solution Approach 2:
The unified model is designed with multi-functionality to handle both detection tasks (identifying street sign locations and objects) and recognition tasks (extracting text content) within a single system framework. This universal approach allows the system to perform multiple functions that previously required separate specialized models, optimizing resource utilization and reducing processing delays
2Reliability
If separate detection and recognition models are used in sequence, then comprehensive processing is achieved, but memory resources and processing time increase
Solution Approach 1:
The patent combines multiple processing functions (detection and recognition) into a single integrated model architecture, reducing system complexity by eliminating redundant components. The unified model shares common feature extraction layers and processing pipelines, thereby reducing overall system complexity while maintaining comprehensive processing capabilities through coordinated multi-task learning
3Measurement precision
If traditional sequential processing is used for street sign detection and recognition, then accurate processing is achieved, but processing speed decreases
Solution Approach 1:
The patent transforms the sequential processing approach into a parallel processing architecture by introducing a dimensional change in the data flow structure. The unified model processes detection and recognition tasks simultaneously through parallel branches that share common features, enabling accurate processing while significantly improving processing speed through concurrent computation rather than sequential execution
Data Source
AI summary
The invention relates to a method for detecting and recognizing (31) a street sign (6) in an environment (5) of a motor vehicle (1) by an assistance system (2) of the motor vehicle (1), comprising the steps capturing at least one image (9) of the environment (5) by an optical capturing device (3) of the assistance system (2), encoding the captured image (9) by a transformer device (12) of an electronic computing device (4) of the assistance system (2), first decoding of the encoded image (9) by a detection transformer device (13) of the electronic computing device (4) for decoding object features (15) in the captured image (9), second decoding of the encoded image (9), wherein the second decoding is performed in parallel to the first decoding, by a recognition transformer device (14) of the electronic computing device (4) for text recognition (16) in the captured image (9) and detection and recognition (31) of the street sign (6) depending on the decoded object features (15) and the text recognition (16) by the electronic computing device (4). Further the invention relates to a computer program product, a computer-readable storage medium, as well as an assistance system (2).