AI Vehicle Damage Visualization Without Specialized Imaging Equipment
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Solution Overview
Problem
Vehicle dealers face challenges in providing uniform vehicle images for online sales, as non-uniform image characteristics increase computing resource consumption, and specialized equipment is costly and resource-intensive.
Innovation Solution
An image annotation platform uses artificial intelligence techniques to standardize and identify vehicle parts and damage, generating annotated images that conserve computing resources and eliminate the need for specialized equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If vehicle dealers use specialized equipment to capture uniform vehicle images, then image uniformity and quality are improved, but device cost and resource consumption increase
Solution Approach 1:
The patent replaces mechanical/image capture system uniformity requirements with computational processing. Instead of requiring specialized cameras and controlled lighting environments, the system uses AI-based image processing algorithms to analyze and standardize images captured by ordinary devices, substituting mechanical precision with computational intelligence.
Solution Approach 2:
The system changes the parameters of image processing by applying AI techniques that adaptively adjust image characteristics. The processor analyzes image parameters such as lighting conditions, angles, and perspectives, then transforms them into standardized representations, allowing uniform output from variable input conditions without specialized capture equipment.
2Device complexity
If vehicle dealers process non-uniform images without standardization, then resource consumption increases, but equipment investment is reduced
Solution Approach 1:
The system performs preliminary standardization processing on images before they are used for vehicle assessment or display. By pre-processing images to extract and standardize key features using AI techniques, the system reduces the computational burden on subsequent processing stages, thereby lowering overall resource consumption while maintaining equipment simplicity.
3Measurement precision
If AI techniques are used to identify vehicle parts and damage, then damage visualization accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent introduces an intermediary AI processing layer between image capture and damage assessment. This intermediary system uses machine learning models to automatically identify vehicle parts, detect damage, and generate standardized annotations, bridging the gap between raw images and actionable insights while managing processing complexity through modular architecture.
Data Source
AI summary
A device may receive images of an object and information identifying the object, process the images using an artificial intelligence technique to identify parts of the object that are depicted in the images, and receive information identifying a location of damage on the object and information regarding the damage on the object. The device may process the information identifying the location of damage to identify a damaged part of the object, identify images depicting the damaged part, and identify, in the images, a location of the damaged part. The device may generate a first content item for display at the location of the damaged part in the images and generate a second content item for display with the images based on user interaction with the first content item, where the second content item includes information based on the information regarding the damage on the object.