Vehicle Damage Detection via Spatiotemporal Correlation
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Solution Overview
Problem
Current image processing systems struggle to accurately detect and analyze damage on vehicles, such as scratches and dents, by identifying redundant damage instances across multiple images captured from different views and at different times, leading to incorrect assessments of damage extent and location.
Innovation Solution
A computer-implemented method and system that perform spatiotemporal correlation between time-spaced image sequences from multiple image sensors positioned at various views, identifying redundancy in candidate damage regions to pinpoint a single physical damage location, and provide an indication of the common physical location, allowing for accurate damage detection and recommendation generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple image sensors capture images from different views at different times, then the coverage and detection capability improve, but redundant damage instances are generated leading to assessment errors
Solution Approach 1:
The patent merges multiple candidate damage regions that correspond to the same physical location across different images and time points into a single unified damage region. This is achieved through spatiotemporal correlation that combines spatial coordinates from different camera views with temporal information to identify and consolidate redundant detections, thereby eliminating duplicate damage instances while preserving accurate damage information.
Solution Approach 2:
The patent creates a virtual 3D model of the vehicle as a copy of the physical vehicle, and maps detected damage regions onto this virtual model. This allows redundant damage detections from multiple camera views to be correlated and merged in the virtual space, where each physical damage location corresponds to a unique location on the virtual model, thus eliminating redundancy while maintaining detection accuracy.
2Productivity
If candidate damage regions are identified in multiple time-spaced image sequences, then damage detection coverage improves, but identifying and eliminating redundant instances increases processing complexity
Solution Approach 1:
The patent performs preliminary actions by first identifying candidate damage regions in individual images before performing spatiotemporal correlation. The system pre-processes each image to detect potential damage, then consolidates these candidates across multiple images and time points. This preliminary identification simplifies the subsequent correlation process by reducing the search space and focusing computation on relevant candidate regions rather than processing all pixels.
Solution Approach 2:
The patent segments the damage detection process into distinct stages: (1) candidate region identification in individual images, (2) spatiotemporal correlation to identify redundant instances, and (3) consolidation into unique damage regions. This segmentation allows each stage to be optimized independently, improving overall processing efficiency while managing complexity through modular design.
3Measurement precision
If redundancy identification is performed across multiple physical components, then comprehensive damage mapping improves, but the computational load and processing time increase
Solution Approach 1:
The patent transitions from 2D image space to 3D virtual model space to perform redundancy identification. By mapping damage candidates onto a 3D virtual model of the vehicle, the system exploits the additional spatial dimension to efficiently correlate damage locations across multiple camera views and time points. This dimensional transformation allows the system to identify redundant instances more quickly by checking spatial coincidence in 3D space rather than comparing 2D image coordinates directly.
Data Source
AI summary
There is provided a computer implemented method of image processing for detection of damage on a vehicle, comprising: accessing a plurality of time-spaced image sequences depicting a region of a vehicle, captured by a plurality of image sensors positioned at a plurality of different views, identifying a plurality of candidate regions of damage in the plurality of time-spaced image sequences, performing a spatiotemporal correlation between the plurality of time-spaced image sequences, identifying redundancy in the plurality of candidate regions of damage corresponding to a common physical location of the vehicle denoting a single physical damage region, and providing an indication of the common physical location of the vehicle corresponding to the single physical damage region.