Object Annotation Scoring for Faster High-Precision Labeling
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Solution Overview
Problem
Existing methods for annotating objects in image, video, and point cloud data require high levels of manual effort and cost due to the need for extensive training data, leading to inefficient and costly annotation processes.
Innovation Solution
A computer-implemented method for partially automated identification and annotation of objects, calculating a numerical value based on the correspondence between visual and conceptual dimensions, and sensor properties, enabling precise and efficient annotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation methods are used to ensure high accuracy in object identification and classification, then annotation precision is improved, but annotation time and cost increase significantly
Solution Approach 1:
The patent replaces manual mechanical annotation processes with an automated computer vision system that uses machine learning models to identify, annotate, and classify objects in images. The system automatically generates bounding boxes, segmentations, and class labels without human intervention, thereby substituting human labor with automated computational processes.
Solution Approach 2:
The system enables self-service annotation by allowing the computer vision model to automatically annotate its own training data without requiring external human annotators. The automated pipeline includes object detection, bounding box generation, and classification all performed by the system itself, making the annotation process independent of manual intervention.
2Reliability
If extensive manual annotation is performed to create large training datasets, then model training reliability is improved, but personnel and cost expenses increase
Solution Approach 1:
The patent replaces expensive manual annotation services with automated computer vision algorithms. The system uses pre-trained models to perform object detection and classification, generating annotated training data at a fraction of the cost of human annotators while maintaining sufficient reliability for model training.
Solution Approach 2:
The system performs preliminary automated annotation to generate initial training datasets before formal model training begins. This pre-annotation step creates a foundation of labeled data that can be used for initial model training or further refined with minimal human intervention, reducing the overall cost and time required for data preparation.
3Productivity
If automated annotation methods are used to reduce manual effort, then productivity is improved, but annotation precision may deteriorate
Solution Approach 1:
The patent employs sophisticated computer vision models and machine learning algorithms to perform automated annotation with high precision. The system uses state-of-the-art object detection models that can accurately identify and classify objects, maintaining annotation quality comparable to or exceeding manual methods while dramatically improving productivity.
Data Source
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AI summary
The invention relates to a computer-implemented method for assigning a numerical value to an annotation (10a, 10b) of at least one object (14a, 14b) identified in image, video, and/or point cloud data (12). The invention further relates to a system for assigning a numerical value to an annotation (10a, 10b) of at least one object (14a, 14b) identified in image, video, and/or point cloud data (12). The invention also relates to a computer program.