Image Annotation Propagation for Faster Damage Model Training
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Solution Overview
Problem
Accurate automated damage assessment models require large amounts of manually labeled training data, which is time-consuming and prone to human error, leading to incorrect model training and increased costs.
Innovation Solution
Automated propagation of annotations from a single labeled image to multiple images using techniques such as plane-to-plane mapping, camera pose estimation, and learning dense visual alignment, allowing for efficient generation of training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling of training data is performed, then annotation accuracy can be ensured, but time consumption and cost increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically propagating annotations from a small set of manually labeled images to generate a large volume of training data. This preliminary automatic propagation reduces the overall time consumption while maintaining acceptable annotation quality for model training purposes.
Solution Approach 2:
The invention creates copies of annotations by propagating label information from source images to target images through image matching and transformation techniques. This copying process generates additional training data without requiring manual annotation of each individual image, thus reducing time consumption while preserving annotation accuracy.
2Reliability
If manual labeling of training data is performed, then data quality can be controlled, but human error risk increases
Solution Approach 1:
The system performs self-service by using automated algorithms to propagate annotations and generate training data without human intervention. This eliminates human error in the annotation process while maintaining data quality through consistent application of image matching and transformation algorithms across all generated labels.
Solution Approach 2:
The invention replaces the mechanical human annotation process with automated computational algorithms for image matching, transformation, and annotation propagation. This substitution eliminates human error while maintaining data quality through systematic and consistent algorithmic processing.
3Measurement precision
If large amounts of training data are required for accurate damage assessment models, then model accuracy improves, but annotation cost increases
Solution Approach 1:
The system performs preliminary automatic annotation propagation to generate a large volume of training data from a small seed set of manually labeled images. This preliminary action provides sufficient training data for accurate model training while avoiding the prohibitive cost of manually annotating every training sample.
Solution Approach 2:
The invention creates numerous copies of annotation information by propagating labels from source images to multiple target images. This copying approach generates the large volume of training data needed for accurate damage assessment models at a fraction of the cost of manual annotation.
4Productivity
If automated annotation propagation is implemented, then productivity increases, but annotation precision may deteriorate
Solution Approach 1:
The invention replaces manual annotation with automated computational algorithms for image matching and annotation propagation. This substitution dramatically increases productivity while maintaining acceptable precision through systematic application of transformation algorithms and image correspondence techniques.
Solution Approach 2:
The system performs preliminary quality control by validating image matches and transformations before finalizing propagated annotations. This preliminary validation ensures that automated propagation maintains sufficient precision for model training while achieving high productivity through automation.
Data Source
AI summary
A plurality of images of an object may be processed. The plurality of images may comprise a first image having an annotation. The plurality of images may further comprise second images. The annotation may be absent from the second images. A placement of the annotation for the second images such that the annotation is configured to be included in the second images may be automatically determined. The second images may be caused to include the annotation in accordance with the determined placement. The annotated second images may be stored on a storage device.