Vehicle Classification Using Registration Data for Automated Labeling
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Solution Overview
Problem
Current image analysis systems for vehicle classification using deep learning face challenges in efficiently classifying a large number of vehicle types and updating algorithms to accommodate new vehicles, due to the need for extensive labeled data and the difficulty in securing sufficient data for each type, especially with the increasing variety of vehicles on the road.
Innovation Solution
A device and system that acquires vehicle images, extracts vehicle identification information, and uses vehicle registration information to generate classification keys for categorizing vehicles, allowing for automated labeling and learning without human intervention, enabling efficient classification and quick adaptation to new vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is used to provide learning data for vehicle classification, then labeling accuracy can be ensured, but time consumption and cost increase significantly
Solution Approach 1:
The system enables vehicles to automatically generate their own classification labels by extracting features from images and matching them with registration information, eliminating the need for manual human labeling while maintaining high accuracy through automated feature extraction and comparison processes
Solution Approach 2:
The patent replaces the mechanical manual labeling process with an automated computer vision system that uses image processing, feature extraction, and database matching to generate labels automatically, substituting human labor with computational processes
2Measurement precision
If extensive labeled data is collected for each vehicle type to improve deep learning performance, then classification accuracy improves, but data collection difficulty and time increase
Solution Approach 1:
The system performs preliminary classification and labeling for each vehicle type before training the deep learning model, using automated feature extraction and registration information matching to prepare labeled datasets in advance, which then serves as training data for improved classification accuracy
Solution Approach 2:
Each vehicle type automatically generates its own training data through automated image processing and feature extraction, eliminating the need for manual data collection and labeling efforts while ensuring sufficient labeled data is available for each class
3Adaptability or versatility
If the vehicle classification system is updated to accommodate new vehicles, then adaptability improves, but system complexity and update difficulty increase
Solution Approach 1:
The system dynamically adapts to new vehicle types by continuously learning from newly collected images and registration information, allowing the classification model to evolve and accommodate emerging vehicle models without requiring complete system redesign or complex manual updates
Data Source
AI summary
A vehicle classification device using an image analysis, according to one embodiment of the present invention, is presented. The device comprises: an image acquisition unit for acquiring a vehicle image including a license plate; a vehicle information acquisition unit for extracting vehicle identification information from the acquired vehicle image; a registration information acquisition unit for acquiring vehicle registration information corresponding to the vehicle identification information; a control unit which allocates a classification key by selectively extracting some of the vehicle registration information indicating external characteristics of a vehicle, and which classifies the vehicle image according to the classification key; and a learning processing unit using the classification key to learn the vehicle image.