Panoptic Road Hazard Segmentation Across Mixed Annotation Datasets
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Solution Overview
Problem
Panoptic segmentation tasks require costly human intervention for large-scale ground truth annotations, and existing datasets often have varying and incomplete annotations, making it difficult to combine them effectively.
Innovation Solution
A computer-implemented method using a segmentation model with an image branch and a text branch that embeds images and text into a joint latent space, generating masks and determining probabilities for road hazards, allowing for the integration of multiple datasets with different annotation schemas and enabling the recognition of novel categories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human intervention is used for ground truth annotations in panoptic segmentation, then segmentation accuracy is improved, but annotation cost increases significantly
Solution Approach 1:
The system uses self-supervised learning where the model trains on unlabeled data by predicting its own annotations. The segmentation model processes images through an image encoder and generates predictions that are used to create pseudo-labels, eliminating the need for expensive manual annotations while maintaining segmentation accuracy.
Solution Approach 2:
The patent creates synthetic ground truth annotations by generating pseudo-labels from model predictions. These synthetic annotations copy the structure and quality of manual annotations without requiring human input, allowing the model to learn from large datasets automatically.
2Adaptability or versatility
If multiple datasets with varying annotation schemas are combined, then dataset diversity and model robustness are improved, but data integration complexity increases
Solution Approach 1:
The system transforms different annotation schemas into a unified parameter space by projecting all annotations into a common embedding space. This allows datasets with varying annotation granularities and categories to be integrated seamlessly by representing them in terms of shared visual features and concepts.
Solution Approach 2:
The patent creates a universal annotation representation framework that can handle multiple annotation schemas simultaneously. The model learns a unified representation that works across different datasets with different annotation styles, making the system universally applicable to diverse data sources.
3Loss of time
If segmentation models are trained on limited datasets, then training time is reduced, but segmentation performance deteriorates
Solution Approach 1:
The system pre-trains the segmentation model on large unlabeled datasets using self-supervised learning before fine-tuning on smaller labeled datasets. This preliminary training enables the model to learn general segmentation capabilities from abundant data, and then quickly adapts to specific tasks with limited annotated data.
Solution Approach 2:
The patent uses excessive unlabeled data (entire datasets without annotations) to train the base model, then applies only partial labeled data for fine-tuning. This approach leverages the abundance of unlabeled data to achieve strong performance while requiring minimal manual annotation effort.
Data Source
AI summary
Methods and systems identifying road hazards include capturing an image of a road scene using a camera. The image is embedded using a segmentation model that includes an image branch having an image embedding layer that embeds images into a joint latent space and a text branch having a text embedding layer that embeds text into the joint latent space. A mask is generated for an object within the image using the segmentation model. A probability is determined that the object matches a road hazard using the segmentation mode. A signal is generated responsive to the probability to ameliorate a danger posed by the road hazard.