Virtual License Plate Generation for Neural Network Training
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Solution Overview
Problem
Current technologies face challenges in identifying license plates attached to vehicles using neural networks, particularly in obtaining accurate position and color information for training purposes without infringing on user privacy.
Innovation Solution
An electronic device configured with a processor and memory, which uses a template to generate a virtual license plate, identifies a real license plate's position and color information, and merges this data to create a third image for training a neural network, ensuring the virtual license plate's color is adjusted based on the real plate's information to simulate a real-world scenario without using actual license plates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real license plate images are used for training neural networks, then training accuracy is improved, but user privacy is compromised
Solution Approach 1:
The patent creates virtual license plate images that copy the essential visual characteristics and structure of real license plates without using actual license plate data. These synthetic images serve as training data for neural networks, maintaining recognition accuracy while eliminating privacy concerns associated with using real license plate images.
Solution Approach 2:
The patent introduces a virtual license plate generation system as an intermediary between real license plates and training requirements. This intermediary creates synthetic training data that bridges the gap between the need for realistic training images and the requirement to protect user privacy, allowing accurate training without direct use of personal data.
2Object-affected harmful factors
If virtual license plates are generated using templates, then privacy protection is improved, but realism and training effectiveness may deteriorate
Solution Approach 1:
The patent applies local quality by varying specific characteristics of virtual license plates such as color, text patterns, and design elements to match different regional standards and real-world variations. This ensures that while the overall structure follows templates, local details maintain realism and diversity necessary for effective training.
Solution Approach 2:
The patent utilizes parameter changes by systematically varying multiple attributes of virtual license plates including color schemes, text formats, sizes, and design parameters to create diverse training datasets. These parameter variations ensure that generated images closely resemble real license plates across different conditions and regions, maintaining training effectiveness.
3Measurement precision
If color information from real license plates is used to adjust virtual plates, then training accuracy is improved, but the risk of inferring real plate information increases
Solution Approach 1:
The patent extracts only the necessary color information characteristics from real license plates without capturing or storing actual plate data. By separating the extraction of general color patterns and characteristics from specific plate identities, the system maintains training accuracy while preventing potential data leakage.
Data Source
AI summary
In an electronic device according to an embodiment, the electronic device, is configured to include memory, and a processor. The processor is configured to obtain a first image including a virtual license plate including text objects, using a template indicating a type of license plates. The processor is configured to, based on identifying a real license plate attached to a vehicle from a second image corresponding to the vehicle, obtain position information of the real license plate and color information regarding at least a portion of the real license plate. The processor is configured to, using the color information, change at least one color of pixels included in the first image.