Vehicle Recognition Using Deep Learning Brand and Color Extraction
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Solution Overview
Problem
Current vehicle recognition technologies face challenges in accurately identifying vehicles when license plates are obscured, stained, or at a distance, leading to low efficiency and high error rates due to reliance on human judgment and traditional image processing methods.
Innovation Solution
A vehicle recognition system utilizing deep learning models, such as convolutional neural networks (CNN), deep belief networks (DBN), and recurrent neural networks (RNN), to extract and recognize vehicle brand and color features from images, prioritizing the vehicle face as the primary feature for brand recognition and employing preprocessing techniques to enhance image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing methods are used for vehicle recognition, then the system is simple to implement, but recognition accuracy decreases when license plates are obscured, stained, or at a distance
Solution Approach 1:
The patent replaces traditional mechanical/image processing methods with deep learning neural networks. Specifically, it uses convolutional neural networks (CNN) for feature extraction and recurrent neural networks (RNN) for sequence recognition, substituting conventional computer vision algorithms with AI-based systems that can handle obscured, stained, or distant license plates more effectively
Solution Approach 2:
The patent introduces an intermediary preprocessing module that enhances image quality before recognition. This module includes operations such as denoising, deblurring, and color correction that act as intermediaries between the raw image and the recognition system, improving the input quality for subsequent deep learning processing
2Productivity
If artificial recognition is used for vehicle identification, then the system can handle complex cases, but recognition efficiency is low and error rates are high
Solution Approach 1:
The patent implements a self-service recognition system where the deep learning model automatically processes and recognizes vehicle license plates without human intervention. The system performs self-correction through the RNN's sequence modeling capabilities, which can identify and correct errors in recognition sequences, achieving both high efficiency and reliability
Solution Approach 2:
The patent incorporates feedback mechanisms in the form of loss functions that guide the neural network learning process. The system uses supervised learning with labeled data, where the output is compared against ground truth and feedback is provided through gradient descent to continuously improve recognition accuracy and efficiency
3Measurement precision
If deep learning models are used for vehicle recognition, then recognition accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the recognition process into distinct stages: image preprocessing, feature extraction using CNN, and sequence recognition using RNN. This segmentation allows each module to be optimized independently, with the CNN extracting spatial features efficiently and the RNN processing temporal sequences, reducing overall processing time while maintaining high accuracy
Solution Approach 2:
The patent performs preliminary actions through the preprocessing module that enhances image quality before the main recognition process. By applying denoising, deblurring, and color correction in advance, the system prepares optimized input data for the deep learning models, reducing the computational burden during recognition and decreasing processing time
Data Source
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AI summary
The present disclosure relates to a vehicle recognition method and system. The vehicle recognition method comprises: receiving a first image; extracting a first vehicle brand recognition region and a first color recognition region of the first image; recognizing the extracted first vehicle brand recognition region by a trained vehicle brand deep learning model; recognizing the extracted first color recognition region; and recognizing the vehicle based on the recognition results of the first vehicle brand recognition region and the first color recognition region.