Autonomous Vehicle Imaging Inspection for Undercarriage Damage Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional vehicle inspection methods are labor-intensive and costly, requiring manual processes that involve moving vehicles to inspection bays and using specialized equipment, leading to inefficiencies and potential inaccuracies in damage identification.
Innovation Solution
The use of remotely-controlled or autonomously operated inspection devices, such as drones or ground vehicles equipped with imaging units, to capture and analyze imaging data for vehicle damage, allowing for automated identification and verification of damages without the need for physical relocation or specialized machinery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection processes are used, then damage identification can be performed, but labor costs and inspection time increase significantly
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated imaging system that uses cameras and computer vision algorithms to detect vehicle damage. The system captures images of the vehicle and automatically analyzes them to identify damages, eliminating the need for manual visual inspection by mechanics and significantly reducing inspection time while maintaining or improving accuracy.
Solution Approach 2:
The inspection system performs self-assessment by automatically analyzing captured images without requiring human intervention for the actual damage detection. The computer vision algorithms independently process the images, identify damage patterns, and generate inspection reports, enabling the system to serve itself in the inspection task.
2Measurement precision
If vehicles are moved to inspection bays and specialized equipment is used, then complete inspection is possible, but equipment costs and operational complexity increase
Solution Approach 1:
The patent extracts the inspection capability from the physical inspection bay environment and embeds it directly into a portable imaging device. By capturing images at the vehicle's current location and performing analysis digitally, the system removes the requirement for specialized inspection bay equipment while maintaining comprehensive inspection capability.
Solution Approach 2:
The system creates digital copies of the vehicle's physical state through high-resolution imaging. These image copies serve as virtual representations that can be analyzed without physically manipulating the vehicle or requiring specialized inspection equipment, thereby simplifying the physical inspection infrastructure needed.
3Measurement precision
If manual inspection is performed, then damage can be identified, but labor costs increase
Solution Approach 1:
The patent substitutes human mechanics with automated computing systems that use machine learning models and image processing algorithms to detect damage. This replacement maintains high detection accuracy while significantly increasing the extent of automation, as the system can process images objectively without fatigue or human error.
Solution Approach 2:
The system incorporates feedback mechanisms where inspection results are continuously refined based on accumulated data from multiple inspections. The automated system learns from previous cases and improves its damage detection accuracy over time, creating a self-improving automated inspection process that reduces reliance on manual expertise.
Data Source
AI summary
One or more processing elements may be trained to identify vehicle damages or vehicle damages based upon training data. A remotely-controlled (RC) and/or autonomously operated inspection device, such as a ground vehicle or drone, may capture one or more sets of imaging data indicative of at least a portion of an automotive vehicle, such as all or a portion of the undercarriage. The one or more sets of imaging data may be analyzed using the trained processing elements to identify a damage to the vehicle or defect of the vehicle.