Neural Network Captioning for Unknown Traffic Signs
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Solution Overview
Problem
Current traffic sign recognition systems rely heavily on large databases and are unable to identify unknown signs, considering them as 'garbage' if not found within the database.
Innovation Solution
An advanced driving assistance system utilizing a combination of a traffic sign classifier and a neural network, specifically a CNN and LSTM network, to analyze images and generate captions for unknown traffic signs by training on a plurality of image data and associated captions for known signs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large database of predefined traffic signs is used for recognition, then recognition accuracy for known signs is improved, but the system cannot identify unknown signs and considers them as garbage
Solution Approach 1:
The patent introduces an intermediary neural network component that bridges the gap between predefined sign recognition and unknown sign identification. The neural network acts as a mediator that receives images from the camera system and generates captions for unknown signs, enabling the system to handle signs not present in the predefined database while maintaining accurate recognition of known signs.
Solution Approach 2:
The system changes the parameter approach from static predefined sign categories to dynamic caption-based identification. Instead of matching images against fixed database entries, the system uses neural networks to generate descriptive captions that can adapt to any sign type, transforming the recognition paradigm from rigid classification to flexible description.
2Device complexity
If a conventional traffic sign classification approach is used, then system simplicity is maintained, but the system cannot provide captions for unknown signs
Solution Approach 1:
The patent segments the traffic sign recognition system into distinct functional components: a camera system for image acquisition, a classification system for known signs, and a neural network module for generating captions of unknown signs. This segmentation allows each component to specialize in specific tasks, maintaining overall system simplicity while enabling comprehensive sign identification and description.
3Ease of manufacture
If traffic signs not found in the database are considered garbage, then database maintenance simplicity is preserved, but useful information about unknown signs is lost
Solution Approach 1:
The neural network component enables the system to self-identify and self-describe unknown signs without requiring manual database updates or maintenance. The system automatically generates captions for unknown signs, effectively serving itself by acquiring and processing new sign information autonomously, eliminating the need for continuous human intervention in database maintenance.
Data Source
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AI summary
The present invention relates to a device (10) for determining a caption for an unknown traffic sign. It is described to provide (310) a processing unit with at least one image data relating to an unknown traffic sign for which a caption associated with the traffic sign is not known. The processing unit implements (320) at least one artificial neural network to process the at least one image data to generate a caption for the unknown traffic sign. The at least one artificial neural network has been trained on the basis of a plurality of image data and associated captions for a plurality of known traffic signs, wherein the caption for each known traffic sign is known. An output unit outputs (330) the caption for the unknown traffic sign.