Automated Vehicle Verification System Using Deep Learning Image Analysis

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Solution Overview

Problem

Manual verification of vehicle authenticity is time-consuming and inefficient, necessitating the development of automated methods and systems for vehicle verification.

Innovation Solution

A computing device-based system that receives vehicle verification information, including images acquired by a client via an imaging device, and determines a verification result based on this information, utilizing a receiving module and a verification module to automate the verification process.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual verification of vehicle authenticity is performed, then verification accuracy can be maintained through human judgment, but verification efficiency is low and the process is time-consuming

Engineering Contradiction:

Improveverification efficiencyVSAvoidverification time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent replaces the manual mechanical verification process with an automated image recognition system using deep learning algorithms. The system automatically captures vehicle images, processes them through neural networks, and generates verification results without human intervention, thereby dramatically improving verification efficiency and eliminating time loss associated with manual procedures

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The verification system performs self-verification by automatically capturing images, analyzing vehicle features, and determining authenticity without requiring manual inspection. The system serves itself by integrating image capture, processing, and decision-making into an autonomous workflow that eliminates dependency on human verifiers

Inventive Principle:

Principle #25Self-service

2Productivity

If automated image recognition is used for vehicle verification, then verification efficiency is improved, but system complexity increases

Engineering Contradiction:

Improveverification efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent segments the verification system into distinct functional modules: image capture module, image preprocessing module, feature extraction module using deep learning, and result generation module. This segmentation allows each component to be independently optimized and maintained, managing system complexity while maintaining high verification efficiency

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system employs a universal deep learning framework that can handle multiple vehicle types and verification scenarios through a single model architecture. The multi-functional approach reduces the need for separate specialized systems for different vehicle categories, thereby controlling complexity while maintaining versatility and efficiency

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12243311B2Methods and systems for vehicle verification

Publication Date: 2025.03.04 BEIJING DIDI INFINITY TECH & DEV CO LTD

AI summary

The present disclosure provides methods and systems for vehicle verification. The method may include receiving vehicle verification information related to a vehicle to be verified from a client, wherein the vehicle verification information includes a plurality of images acquired by the client via an imaging device, and the vehicle verification information responds to at least one vehicle verification instruction. The method may further include determining a verification result of the vehicle based on the vehicle verification information.