Automated Vehicle Recognition Using Neural Network Feature Extraction
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Solution Overview
Problem
Identifying vehicle characteristics, such as year, make, and model from images is a time-consuming and complex task due to the numerous variations in vehicle designs, trim levels, and options across different models and years, especially when including classic cars from the 1950s and earlier.
Innovation Solution
A system utilizing a neural network for feature extraction and a Joint Bayesian signal processing method for classification, which allows a mobile device to detect and classify vehicle characteristics by comparing discriminative features in images against a database, enabling accurate identification of vehicle details like year, make, model, trim, and options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification methods are used to determine vehicle characteristics, then detailed information can be obtained, but the process becomes time-consuming and complex
Solution Approach 1:
The patent replaces manual visual inspection and mechanical identification processes with an automated image processing system using neural networks and Bayesian classification algorithms. The system automatically extracts features from vehicle images, classifies them using probabilistic models, and identifies vehicle characteristics without human intervention, thereby reducing time while maintaining accuracy
Solution Approach 2:
The system creates digital copies of vehicle images and processes them through computational models. By working with image data copies rather than physical vehicles, the system enables rapid automated analysis without the time constraints of manual inspection, while the detailed feature extraction maintains identification precision
2Measurement precision
If comprehensive vehicle features are analyzed to identify trim and options, then identification accuracy improves, but the complexity of the task increases significantly
Solution Approach 1:
The patent segments the complex vehicle identification task into distinct components: image acquisition, feature extraction using neural networks, classification using Bayesian methods, and result synthesis. This segmentation allows each component to be optimized independently, managing overall system complexity while achieving comprehensive vehicle characteristic identification
Solution Approach 2:
The system introduces intermediate processing layers including neural network feature extractors and Bayesian classification models that mediate between raw image data and final vehicle identification. These intermediaries transform complex image data into manageable feature representations, reducing the complexity of direct analysis while preserving identification accuracy
3Adaptability or versatility
If multiple vehicle attributes are identified simultaneously, then the system provides comprehensive information, but the processing complexity increases
Solution Approach 1:
The patent implements a universal classification system that can identify multiple vehicle attributes (year, make, model, trim, options) through a single integrated processing pipeline. The Bayesian classification framework is designed to handle diverse vehicle features simultaneously, providing comprehensive information coverage without requiring separate processing systems for each attribute
Data Source
AI summary
This disclosure describes a device and methods for determining an image. The disclosure further describes devices and methods for detecting a vehicle in the image; extracting at least one first feature from the image; determining a match between each of the at least one first feature and each of at least one second features stored the at least one memory; and determining a ranking of the each of the at least one first feature.