Image Alignment System for Vehicle Damage Detection
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Solution Overview
Problem
Current image processing systems are unable to quickly and effectively detect changes in objects, such as damage to vehicles or buildings, due to difficulties in comparing images from different perspectives, angles, and camera distortions, leading to inaccurate or incomplete change detection.
Innovation Solution
An image processing system that compares a base object model with target images using landmarks and correction filters to align and correct for distortions, followed by statistical processing with convolutional neural networks to quantify changes and display them as heat maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If images of the same object are compared from different perspectives and using different cameras, then the system can handle real-world variability, but distortion and alignment errors increase making accurate change detection difficult
Solution Approach 1:
The patent creates a three-dimensional base model (digital twin) of the object that serves as a reference copy. This base model can be virtually rendered from any perspective or camera angle without introducing actual camera distortions, allowing comparison with target images while maintaining measurement precision.
Solution Approach 2:
The patent transitions from comparing two-dimensional images directly to using a three-dimensional base model. By elevating the reference to 3D space, the system can generate consistent reference views from any angle, eliminating the constraints of actual camera perspectives and enabling accurate change detection across varied imaging conditions.
2Measurement precision
If images are obtained from the same camera to avoid distortion, then measurement accuracy improves, but the system cannot detect changes over time when re-imaging is required
Solution Approach 1:
Instead of using repeated physical camera images, the system creates a virtual copy of the object in 3D space that can be rendered from any perspective. This virtual reference model maintains consistent geometric accuracy while allowing the system to accommodate target images taken at different times with different cameras.
Solution Approach 2:
The base model is created dynamically through 3D scanning or reconstruction processes, and can be virtually re-rendered dynamically from any camera position or angle. This dynamic virtual representation replaces static physical camera constraints, enabling flexible temporal comparisons.
3Productivity
If simple image comparison is performed, then processing speed is fast, but subtle changes such as damage or degradation cannot be detected
Solution Approach 1:
The patent segments the object into multiple three-dimensional surface panels or regions. By dividing the complex change detection problem into smaller localized panel comparisons, the system can efficiently process each region while detecting subtle changes in geometry, reflectivity, or texture that would be missed in global image comparisons.
Solution Approach 2:
The system applies different analysis techniques to different regions or panels of the object based on local characteristics. This allows the system to focus computational resources on detecting subtle local changes in critical areas while maintaining overall processing efficiency.
Data Source
AI summary
An image processing system that may be used to detect changes in objects, such as damage to automobiles, compares a base object model, which depicts the object in an expected condition, to one or more target object images of the object in the changed condition. To do so, the image processing system first processes a target object image to detect one or more predefined landmarks in the target object image using one or more correlation filters. The image processing system then uses the detected landmarks to determine a camera model for the target object image and uses the camera model to correct for camera distortions and to align the target object depicted in a target object image with the object in the base object model to put these objects in a common frame of reference for use in subsequent image processing. The image processing system may then determine contours of the target object within the target object image by overlaying an aligned base object model with the target object image, may remove background pixels or other extraneous information based on this comparison, and may perform a statistical processing routine on the identified target object image to detect changes to the target object as depicted in the target object image as compared to the base object model.