Interactive Vehicle Inspection Interfaces With Anchor-Based AI Tracking
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Solution Overview
Problem
Traditional vehicle inspection methods are time-consuming, subjective, and prone to human error, often failing to capture comprehensive documentation of vehicle condition and missing subtle damages or defects due to inconsistent and inaccurate tracking across multiple camera views.
Innovation Solution
A system employing multiple imaging devices and dual AI models to generate an interactive user interface that maintains accurate tracking and visualization of vehicle damages across multiple viewing angles, using anchor-based tracking algorithms for spatial registration and temporal synchronization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual visual inspection methods are used, then the inspection process is simple and requires minimal equipment, but the inspection is time-consuming, subjective, and prone to human error
Solution Approach 1:
The patent replaces manual visual inspection with an automated computer vision system that uses multiple imaging devices, AI models for anomaly detection, and algorithms for spatial-temporal tracking. This substitution eliminates human subjectivity and error while maintaining inspection comprehensiveness through automated multi-angle capture and processing.
Solution Approach 2:
The system creates a comprehensive digital copy of the vehicle's surface by capturing images from multiple angles and processing them through AI models. This digital representation allows for repeated analysis, storage, and comparison without requiring physical re-inspection, thereby improving both productivity and reliability.
2Loss of information
If multiple camera views are used to capture comprehensive vehicle condition, then the documentation becomes more complete, but the tracking across views becomes inconsistent and inaccurate
Solution Approach 1:
The patent introduces anchor parts as intermediary reference points that connect multiple camera views. These anchor parts serve as common reference frames that enable consistent spatial-temporal tracking across different angles and time points, resolving the tracking inconsistency problem while maintaining comprehensive documentation.
Solution Approach 2:
The system transitions from 2D image analysis to 3D spatial-temporal tracking by incorporating depth information and temporal sequences. This dimensional enhancement allows for accurate localization and tracking of anomalies across multiple views by establishing their positions in a unified 3D space that accounts for camera positions and vehicle geometry.
3Device complexity
If static images or basic video recordings are provided, then the system complexity is reduced, but the ability to allow interactive exploration of detected anomalies is lost
Solution Approach 1:
The patent transforms static inspection results into a dynamic interactive system where users can explore anomalies from multiple angles, zoom in on details, and navigate through temporal sequences. This dynamic interface enhances ease of operation by allowing flexible exploration while the underlying system manages complexity through automated processing and structured data organization.
Solution Approach 2:
The system introduces an interactive user interface as an intermediary layer between the complex AI processing system and the end user. This interface simplifies interaction by presenting processed results in an intuitive format while maintaining access to the full capability of the underlying complex system for detailed analysis when needed.
Data Source
AI summary
A system and method for generating an interactive user interface for inspection visualization. The system includes multiple imaging devices positioned along a inspection passage and at least one processor that executes instructions to: obtain multiple sets of images of vehicle surface segments captured during relative movement between the vehicle and imaging devices; stitch the images into a dataset record mapping vehicle parts and surface anomalies; transform the image data into a moving visual media object using a first generative AI model; compute a mapping record between segmented vehicle parts and target frame areas; and transform the mapping record and visual media object into an interactive interface using a second generative AI model. The interface displays user-selectable markers synchronized with media playback, indicating anomaly locations from multiple viewing angles, and performs data retrieval and display actions based on user selection of anomalies.