Vehicle Information Recognition Using Neural Network Scoring
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Solution Overview
Problem
Current systems for recognizing vehicle information from moving vehicles are inefficient in processing and verifying license plate numbers and DOT numbers, often resulting in errors due to motion, varying vehicle conditions, and lack of accuracy in real-time applications.
Innovation Solution
A vehicle information recognition system utilizing multiple cameras and machine learning algorithms, including neural networks, to capture and process images of license plates and DOT numbers, with a scoring system to verify matches and account for errors, and web scraping to retrieve relevant data from national databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple cameras and machine learning algorithms are used to improve recognition accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The system segments the recognition task into multiple specialized components: multiple cameras capture images from different angles, machine learning algorithms process and verify the captured information, and a scoring system evaluates match confidence. This segmentation allows each component to focus on a specific aspect of the recognition process, improving overall accuracy while managing complexity through modular design.
Solution Approach 2:
The patent introduces an intermediary scoring system that acts as a mediator between the image capture stage and the final recognition output. This scoring system evaluates the confidence of license plate and DOT number matches, allowing the system to filter and verify results before final determination. This intermediary layer improves measurement precision by adding a verification step without requiring complete system redesign.
2Productivity
If real-time processing is implemented to improve productivity, then output per unit time increases, but measurement precision deteriorates
Solution Approach 1:
The system performs preliminary actions by capturing multiple images of moving vehicles before final recognition occurs. The multiple cameras take pictures at different moments as vehicles pass, creating a pool of candidate images. The machine learning algorithms then process these pre-captured images to identify and verify license plates and DOT numbers, ensuring accuracy is established before final real-time output is generated.
Solution Approach 2:
The scoring system provides feedback by evaluating the confidence level of each recognition match. This feedback mechanism allows the system to adjust processing priorities and verify uncertain recognitions, maintaining measurement precision while enabling real-time operation. The feedback loop ensures that low-confidence matches are re-evaluated or flagged for manual review, balancing speed and accuracy.
Data Source
AI summary
Disclosed systems and methods provide automatic recognition of information from stationary and/or moving vehicles. A disclosed system includes an image capture device that captures an image of a vehicle surface and thereby generates image data. A processor circuit receives the image data from the image capture device and may process the image data to determine a Department of Transportation (DOT) number. The processor circuit may control the image capture device to capture a plurality of images, to detect and recognize text characters in each of the plurality of images, and to compare probabilities of likely DOT numbers determined from each of the plurality of images. The processor circuit may be further configured to determine DOT numbers from captured images by processing image data using a machine learning algorithm. The system may be configured to be portable and to perform real-time analysis using an application specific integrated circuit (ASIC).