Automated Labeling Architecture for Object Detection in Unlabeled Image Data
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Solution Overview
Problem
Current methods for creating ground truth annotations for object detection in autonomous driving scenarios are costly and time-consuming, relying heavily on human labor and requiring large datasets, with existing semi-automated approaches limited by the need for pretraining and sequence-dependent data.
Innovation Solution
The proposed solution uses a semantic segmentation model to filter out irrelevant regions and a Bounding Box Sampler to generate proposal crops, combined with few-shot learning to efficiently label bounding boxes, reducing human intervention and making AI models more generalizable to new data without requiring sequenced image data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human annotators manually provide ground truth labels, then labeling accuracy is high, but time and financial cost are inordinately high
Solution Approach 1:
The system enables automated self-labeling by using a semantic segmentation model to generate pseudo-labels for unlabeled image data. The model processes images independently without human intervention, automatically creating ground truth labels through its own computational capabilities, thus eliminating the time-consuming manual annotation process while maintaining acceptable accuracy
Solution Approach 2:
The system creates synthetic ground truth labels by copying the semantic segmentation output as pseudo-labels. Instead of manually creating labels, the system generates them by replicating the segmentation model's region predictions, which then serve as training data for the object detection model
2Measurement precision
If a large dataset with ground truth labels is used, then model training accuracy is improved, but financial expenditure and time cost increase
Solution Approach 1:
The system performs preliminary semantic segmentation on unlabeled image data before object detection training. By pre-processing the data to create pseudo-labels in advance, the system prepares a large annotated dataset without requiring manual annotation, thus enabling extensive training data accumulation that would otherwise be prohibitively costly
Solution Approach 2:
The system generates training data by copying semantic segmentation results as pseudo-labels. This allows the creation of large-scale annotated datasets by replicating the segmentation model's outputs across numerous unlabeled images, thereby expanding the training corpus without proportionally increasing human resource requirements
3Measurement precision
If pre-trained object detection models are used, then detection accuracy is improved, but the system becomes dependent on pre-existing models and cannot adapt to new tasks without additional training data
Solution Approach 1:
The system creates a dynamic training pipeline where the object detection model is continuously retrained using newly generated pseudo-labels from semantic segmentation. This dynamic process allows the model to adapt to new tasks and datasets by incorporating freshly labeled data, eliminating the static limitation of pre-trained models that cannot easily adapt to domain-specific requirements
Solution Approach 2:
The system implements a feedback loop where semantic segmentation outputs are used to create pseudo-labels that then train the object detection model, which in turn generates new segmentation results. This iterative feedback mechanism enables continuous adaptation to new tasks and data distributions, allowing the system to learn from and adapt to domain-specific patterns without requiring manual retraining
Data Source
AI summary
A computer-implemented method for the detection and recognition of objects in unlabeled image data using an automated labelling architecture. The method includes the steps of: proposing bounding-box in every image of the unlabeled image data using a task specific and/or a related task pretrained object detection model and a Bounding Box Sampler module; filtering said bounding boxes for positive object instances; assigning to said filtered bounding boxes a class label using a Few-Shot Classification module; and modifying filtered bounding boxes based on additional class wise attention output from the Few-Shot Classification module.