Object Labeling System Integrating 2D and 3D Data
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Solution Overview
Problem
Current object detection systems rely heavily on manual or semi-manual labeling of training data, requiring significant human resources and often fail to meet accuracy requirements when low-level AI is used without sufficient human input.
Innovation Solution
An object labeling system comprising a first object labeling module for 2D images, a second object labeling module for 3D information, a label integrating module, and an inter-frame tracking module, which generates and integrates labeling results across 2D and 3D data to improve accuracy and reduce manual labeling needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual or semi-manual labeling is used to provide training data, then the accuracy of labeling results is improved, but the consumption of human resources increases significantly
Solution Approach 1:
The system segments the labeling task into multiple independent modules: a first object labeling module for 2D images, a second object labeling module for 3D information, a label integrating module, and an inter-frame tracking module. Each module handles specific aspects of the labeling process, allowing parallel processing and reducing the need for manual intervention while maintaining high accuracy through specialized function distribution.
Solution Approach 2:
The system transitions from traditional 2D image-only labeling to multi-dimensional labeling by incorporating both 2D image data and 3D information. The second object labeling module processes 3D information to generate additional labeling dimensions, which when integrated with 2D labeling results, provides more comprehensive and accurate object identification without requiring proportional increases in manual labeling effort.
2Productivity
If low-level artificial intelligence is used for object labeling to reduce human resources, then the consumption of human resources is reduced, but the accuracy of labeling results becomes difficult to meet requirements
Solution Approach 1:
The system merges multiple AI labeling modules (first object labeling module for 2D, second object labeling module for 3D) with post-processing modules (label integrating module and inter-frame tracking module) to create a comprehensive automated labeling system. This combination of multiple AI approaches compensates for the limitations of individual low-level AI systems, achieving high accuracy without requiring manual intervention.
Solution Approach 2:
The inter-frame tracking module implements feedback mechanisms by analyzing temporal relationships across video frames. It uses tracking information from previous frames to refine and correct labeling results, providing continuous feedback that improves accuracy automatically without human intervention. This feedback loop allows the system to self-correct and maintain high labeling standards.
3Device complexity
If a single 2D image labeling approach is used, then the system complexity is kept simple, but the labeling accuracy and completeness are limited
Solution Approach 1:
The system achieves multi-functionality by designing modules that can process different types of input data (2D images and 3D information) through similar processing architectures. The first and second object labeling modules serve universal purposes by handling different data dimensions, while the label integrating module universally combines their outputs. This multi-functional design enhances accuracy without proportionally increasing system complexity.
Data Source
AI summary
An object labeling system includes a first object labeling module, a second object label model, a label integrating module and an inter-frame tracking module. The first object label module is configured to generate a first object labeling result according to a first 2D image, wherein the first 2D image is one of the frames of a 2D video. The second object labeling module is configured to generate a second 2D image according to a 3D information, and to generate a second object labeling result according to the 3D information and the second 2D image. The label integrating is configured to generate a third object labeling result according to the first object labeling result and the second object labeling result. The inter-frame tracking module is configured to perform an inter-frame object labeling process according to the third object labeling result to generate a fourth object labeling result.