Road Sign Content Prediction for ML Training Data
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Solution Overview
Problem
Current road sign recognition systems face challenges in developing universal classifiers due to the vast number of diverse road signs across countries, requiring large annotated datasets and relying heavily on human effort for data labeling, which is time-consuming and costly.
Innovation Solution
A system that utilizes a user interface, storage, and processor to detect road signs, execute a visual attribute prediction model, query a knowledge graph reasoner, and output sign candidates from stored templates, automating the selection and annotation process to efficiently manage big data for training machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation and categorization of road sign images is performed to create training datasets, then dataset quality and accuracy are improved, but time consumption and labor costs increase significantly
Solution Approach 1:
The system enables self-service annotation by training a machine learning model to automatically detect road signs, predict their categories, and generate bounding boxes. The model processes images through multiple stages (region proposal, classification, refinement) to produce annotated results without human intervention, thus achieving accurate annotation while eliminating time-consuming manual labor
Solution Approach 2:
The patent replaces the mechanical process of manual annotation with an automated machine learning system. The model uses computational algorithms to perform detection, classification, and bounding box generation, substituting human manual operations with automated mechanical/computational processes that are faster and more scalable
2Adaptability or versatility
If a large number of road sign images are collected to cover diverse categories and countries, then model training coverage is improved, but data management complexity increases
Solution Approach 1:
The patent segments the data management process into modular components: image collection, automated annotation, category classification, and quality validation. Each component handles a specific aspect of data processing, making the overall system more manageable. The model processes images through distinct stages (region proposal, classification, bounding box refinement) rather than as a monolithic process
Solution Approach 2:
The machine learning model is designed to be universal and multi-functional, capable of detecting and categorizing road signs from multiple countries and conventions (Vienna, SADC, SIECA, MUTCD). A single model framework handles diverse sign types and styles, eliminating the need for separate management systems for each category or region
3Productivity
If automated machine learning models are used for road sign detection, then processing speed is improved, but annotation accuracy may deteriorate without proper validation
Solution Approach 1:
The patent implements feedback mechanisms to validate and improve automated annotation accuracy. The system includes quality validation steps where model predictions are checked against ground truth data or expert-verified samples. Loss functions measure prediction accuracy and guide model refinement, creating a feedback loop that continuously improves annotation quality while maintaining high processing speeds
Solution Approach 2:
The system performs preliminary actions to ensure accuracy before final output: it generates multiple region proposals, applies classification filters, and refines bounding boxes through iterative optimization. These preliminary processing steps validate predictions early in the pipeline, ensuring accurate annotations are produced before being used for training, thus maintaining both speed and precision
Data Source
AI summary
Systems and method for machine-learning assisted road sign content prediction and machine learning training is disclosed. A sign detector model processes images or video with road signs. A visual attribute prediction model extracts visual attributes of the sign in the image. The visual attribute prediction model can communicate with a knowledge graph reasoner to validate the visual attribute prediction model by applying various rules to the output of the visual attribute prediction model. A plurality of potential sign candidates are retrieved that match the visual attributes of the image subject to the visual attribute prediction model, and the rules help to reduce the list of potential sign candidates and improve accuracy of the model.