Auto-Labelling Engine for Bounding Box Annotation at Scale
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Solution Overview
Problem
Conventional manual bounding box annotation methods for object identification in images are time-consuming and inefficient, especially for large datasets, requiring significant effort and taking months to years to complete.
Innovation Solution
A system and method utilizing an auto-labelling engine, trained iteratively with a combination of manually annotated and unannotated datasets, to automatically label objects in electronic media, with performance feedback loops to ensure accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual bounding box annotation is used for object identification, then annotation accuracy can be maintained, but the time required increases significantly for large datasets
Solution Approach 1:
The system uses a trained neural network model to generate automated bounding box annotations that copy the pattern of manual annotations. The model learns from a small set of manually annotated images and reproduces the annotation process for large datasets, achieving both speed and acceptable accuracy without requiring manual drawing for every image.
Solution Approach 2:
The patent replaces the mechanical manual drawing process with an automated neural network-based system. Instead of human operators manually creating bounding boxes, a trained AI model automatically detects objects and generates annotations, substituting human cognitive and motor functions with computational processes.
2Productivity
If automated labelling is used to reduce time, then productivity increases, but labelling accuracy may decrease
Solution Approach 1:
The system incorporates feedback mechanisms where the neural network's automated annotations are reviewed and corrected using a small portion of manually annotated data. The model continuously learns from these feedback loops, adjusting its parameters to improve accuracy while maintaining high processing speeds for large datasets.
Solution Approach 2:
The patent employs parameter changes in the neural network model, including adjusting learning rates, batch sizes, and model architecture parameters during training. These parameter optimizations enable the model to achieve high accuracy in automated annotation while maintaining the speed advantage over manual methods.
3Measurement precision
If iterative training with multiple datasets is used, then model accuracy improves, but training complexity and time increase
Solution Approach 1:
The training process is segmented into multiple stages where the neural network is trained on progressively larger and more diverse datasets. The system starts with a small seed dataset of manually annotated images, then iteratively adds more data in manageable batches, allowing the model to learn patterns gradually without overwhelming complexity in a single training run.
Solution Approach 2:
The system performs preliminary training on a small curated dataset to establish the basic annotation patterns and model architecture. This preliminary action creates a foundation that guides subsequent training on larger datasets, reducing the overall complexity by breaking down the training process into manageable preliminary steps.
Data Source
AI summary
The improved system and method disclosed herein includes training an auto-labelling engine in a plurality of iterations until a performance of the auto-labelling engine is satisfactory. The auto-labelling engine is trained based on a combination of a plurality of manually annotated first media and a plurality of second media without annotation. The method further includes receiving at least one media for labelling and using the trained auto-labeling engine to draw at least one bounding box around at least one object in the received at least one media for labelling the at least one object in the at least one media.