Neural Network Vehicle Part Identification System
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Solution Overview
Problem
Existing methods for identifying and pricing used vehicle parts rely on human visual identification, leading to inconsistencies in part categorization and pricing, resulting in inaccurate valuation and potential waste.
Innovation Solution
A device and method utilizing a neural network model, specifically a U-net model, for image analysis to objectively identify and categorize vehicle parts from images, providing accurate part information for pricing and delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human visual identification is used to categorize and price used vehicle parts, then the process is simple and requires minimal technology, but the pricing accuracy and categorization consistency deteriorate due to subjective human judgment
Solution Approach 1:
The patent replaces the mechanical human visual identification system with an automated image processing system using neural networks. The system captures images of vehicle parts, processes them through convolutional neural networks to extract features, and automatically determines part categories and pricing. This substitution eliminates subjective human judgment while maintaining operational simplicity through automation.
Solution Approach 2:
The patent introduces an intermediary image processing system that acts as a mediator between the physical vehicle part and the pricing decision. The system uses image capture devices, preprocessing modules, and neural network models as intermediaries to objectively analyze part characteristics and determine pricing, removing the direct subjective human judgment from the process.
2Measurement precision
If automated image processing with neural networks is implemented to improve pricing accuracy, then pricing precision improves, but the device complexity and implementation cost increase
Solution Approach 1:
The patent segments the image processing system into distinct functional modules: image capture module, preprocessing module (with normalization and feature extraction), neural network processing module (with multiple layers including convolutional and fully connected layers), and output module. This segmentation allows each module to be optimized independently and simplifies implementation and maintenance of the overall complex system.
Solution Approach 2:
The patent implements preliminary actions in the form of image preprocessing steps before neural network processing. The system performs image normalization, feature extraction, and data augmentation in advance to prepare the input data. This preliminary processing improves the efficiency and accuracy of the subsequent neural network processing while managing system complexity through structured preparation.
3Reliability
If manual pricing methods are used, then the implementation is straightforward and requires minimal resources, but the consistency and reliability of part categorization deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where the neural network model is trained using labeled data and continuously improved through feedback from accurate part categorizations. The system learns from training datasets, adjusts its parameters, and refines its categorization accuracy over time. This feedback loop ensures consistent and reliable categorization while the automated process maintains high productivity.
Solution Approach 2:
The patent enables the system to serve itself through automated image processing and neural network-based decision making. The system automatically captures images, processes them through multiple neural network layers, extracts features, and determines part categories without requiring manual intervention. This self-service capability ensures consistent categorization while maintaining high processing efficiency.
Data Source
AI summary
A device for identifying a part of a vehicle is introduced. A device may comprise a processor, memory storing instructions, when executed by the processor, cause the device to receive a first image, pre-process the first image to output a second image, provide the second image to a neural network model that extracts features from the second image, and outputs, based on the extracted features, information associated with a recognized part of a vehicle, store the information associated with the recognized part of the vehicle as vehicle part information, and cause, based on the vehicle part information, a delivery of the recognized part of the vehicle.