Vehicle Undercarriage Imaging for Reliable Defect Inspection
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Solution Overview
Problem
Buyers of vehicles often lack accurate and efficient means to inspect vehicles, especially in online sales environments, as personal inspection is not feasible, leading to reliance on potentially inaccurate vehicle condition reports.
Innovation Solution
A vehicle inspection system comprising multiple sensor arrays and a computer hardware processor that captures and processes images from various angles, using machine learning to identify defects and generate condition reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensor arrays and cameras are used to capture images from various angles, then measurement precision and completeness of vehicle inspection is improved, but device complexity increases
Solution Approach 1:
The inspection system is divided into multiple independent sensor arrays (first sensor array for front/rear, second sensor array for sides, third sensor array for roof) each capturing images from specific angles. This segmentation allows comprehensive coverage while maintaining modular architecture that manages complexity through functional decomposition.
Solution Approach 2:
The system transitions from single-point inspection to multi-dimensional imaging by deploying sensors at various spatial positions (front, rear, sides, roof) and orientations. This dimensional expansion enables comprehensive vehicle surface coverage and defect detection from multiple perspectives simultaneously.
2Reliability
If machine learning processing is applied to identify defects, then reliability of defect detection is improved, but loss of time in processing images increases
Solution Approach 1:
Machine learning models are pre-trained on extensive datasets of vehicle defects before deployment. This preliminary training enables the models to rapidly classify and identify defects during actual inspection without requiring complex real-time analysis, thus maintaining high reliability while reducing processing time.
Solution Approach 2:
The patent replaces manual visual inspection with automated machine learning-based image analysis. This substitution eliminates human subjectivity and variability while providing consistent, reliable defect detection across all vehicles inspected, significantly improving detection reliability.
Data Source
AI summary
Systems and techniques for inspecting undercarriages of vehicles. Some embodiments provide a vehicle undercarriage inspection system. The vehicle undercarriage inspection system may include a base having a width and a depth respectively extending along first and second orthogonal directions, the width extending from a first end to a second end of the base. The vehicle undercarriage inspection system may include a sensor array coupled to the base, the sensor array including a set of cameras oriented in multiple directions. The set of cameras may include: a first camera oriented in part towards the first end of the base, a second camera oriented in part towards the second end of the base, and a third camera between the first and second. The vehicle undercarriage inspection system may include a processor configured to control the sensor array to capture images of an undercarriage of a vehicle with the set of cameras.