Generic License Plate Detection via Multi-Region CNN Training
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Solution Overview
Problem
Existing license plate detection systems face challenges in complex scenarios such as shadows, noise, dust, partial overlap with other objects, low contrast, and varying license plate standards, leading to reduced accuracy, especially when operating on edge devices without GPU capabilities.
Innovation Solution
A system utilizing a Convolutional Neural Network (CNN) based approach for license plate detection, which includes a generic license plate detector module that can be trained with data from multiple countries, enabling detection across different regions with customized license plate standards, and operates efficiently on devices with limited CPU capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If computer vision-based techniques are used for license plate detection, then the system can operate in simple situations, but the accuracy is compromised in complex situations such as shadows, noise, dust, and partial overlap
Solution Approach 1:
The patent segments the license plate detection task into multiple specialized sub-models, each trained to handle specific complex situations (e.g., one model for shadows, another for dust, another for partial overlaps). This segmentation allows each model to specialize in particular challenging conditions, thereby improving overall detection accuracy across diverse complex scenarios while maintaining operational reliability.
2Measurement precision
If multiple specialized detectors are trained for different regions and license plate standards, then the detection accuracy improves, but the time to prepare ground-truth data increases
Solution Approach 1:
The patent implements preliminary action by creating a generic license plate detector that is pre-trained on diverse multi-region data before being specialized into region-specific detectors. This preliminary training establishes a strong foundational model that accelerates the subsequent specialization process, reducing the ground-truth preparation time required for multiple specialized detectors while maintaining high detection accuracy across different regions and license plate standards.
3Productivity
If a generic license plate detector is trained with multi-region data, then the ground-truth preparation speed improves, but the model complexity increases
Solution Approach 1:
The patent applies universality by designing a generic license plate detector with multi-functionality that can handle multiple regions and license plate standards simultaneously. This universal model serves as a foundation that can be efficiently adapted to specific regions through transfer learning, thereby accelerating ground-truth preparation across multiple regions without proportionally increasing model complexity. The single generic model performs multiple detection functions that would otherwise require separate specialized models.
Data Source
AI summary
A system for detecting license plates is described. The system receives raw data comprising images of license plates. A base version of a ground truth is prepared based on the raw data, using a generic license plate detection (LPD). The system prepares input data for training a deep learning network. The deep learning network is trained with the prepared input data. A newly trained generic (LPD) is formed using data generated by the existing generic (LPD).