Image Labeling Setup With Domain Randomization for Training Data
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Solution Overview
Problem
Current image labeling processes for machine learning models are time-consuming and expensive, requiring thousands of manually labeled images and retraining when conditions change, such as object positions, lighting, or background textures.
Innovation Solution
An automated image labeling system comprising a digital camera, a digital display, and a process and control apparatus that captures images, generates labeling data, and introduces variability through background changes and object rearrangement, reducing human intervention and enabling efficient data collection for machine learning model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual image labeling is performed by humans, then labeling accuracy and quality are maintained, but time consumption and costs increase significantly
Solution Approach 1:
The system enables automatic self-labeling by capturing images, detecting objects, and generating labeling data without human intervention. The digital camera captures images, the process and control apparatus generates labeling data including object positions and categories, creating a self-sufficient labeling pipeline that eliminates manual human labor while maintaining systematic quality control
Solution Approach 2:
The patent replaces the mechanical human labeling process with an automated digital system. The digital camera and process and control apparatus substitute human operators, using computational algorithms to detect objects and generate labeling data automatically, thereby reducing time consumption while preserving labeling quality through systematic processing
2Quantity of substance
If thousands of images are manually labeled to train deep learning models, then model training data availability is ensured, but human resources and expenses increase
Solution Approach 1:
The system automatically generates large volumes of labeled images through self-service operation. The digital camera continuously captures images while the process and control apparatus automatically generates corresponding labeling data, enabling the system to produce thousands of labeled images without requiring human operators for each image, thereby ensuring data availability while reducing human resource requirements
Solution Approach 2:
The system performs preliminary automatic labeling actions to prepare training data in advance. By capturing images and generating labeling data before model training requirements are fully established, the system pre-prepares large datasets that can be used for training deep learning models, ensuring data availability while eliminating the need for last-minute manual labeling efforts
3Adaptability or versatility
If Domain Randomization is implemented to introduce variability in training data, then model adaptability to different conditions improves, but system complexity increases
Solution Approach 1:
The system implements dynamic variability introduction through the digital display that changes background images and through object rearrangement mechanisms. These dynamic changes create diverse training scenarios automatically, enabling models to adapt to different conditions such as varying backgrounds, lighting, and object positions without requiring complex manual intervention for each scenario
Solution Approach 2:
The system uses the digital display to copy and present multiple background variations to the objects. By displaying different background images on the digital display behind objects, the system creates multiple copies of the same scene with varying backgrounds, enabling Domain Randomization while maintaining a relatively simple physical setup that doesn't require multiple physical environments
Data Source
AI summary
It is described an image labeling system (100) comprising: a support (2) for an object (3) to be labeled; a digital camera (1) configured to capture a plurality of images of a scene including said object (3); a process and control apparatus (5) configured to receive said images and generate corresponding labeling data (21-24, L1-L4) associated to said object (3); a digital display (4) associated with said support (2) and connected to the process and control apparatus (5) to selectively display additional images (7-13) selected from the group comprising: first images (7-11) in the form of backgrounds for the plurality of images and introducing a degree of variability in the scene; second images (12) indicating position and/or orientation according to which place said object (3) by a user on the support (2); third images (13) to be captured by the digital camera (1) and provided to the process and control apparatus (5) to evaluate a position of the digital camera (1) with respect the digital display (4); fourth images to be captured by the digital camera (1) and provided to the process and control apparatus (5) to evaluate at least one of the following data of the object (3): position, orientation, 3D shape.