End-to-end panoptic segmentation with integrated clustering-based instances separation
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Solution Overview
Problem
Existing automated driving systems face challenges in reliably processing and interpreting sensory information from diverse vehicle environments, including moving and static objects, which requires efficient and resource-effective solutions for accurate navigation.
Innovation Solution
A system for panoptic segmentation using a shared encoder for feature extraction, combined with dual decoders for semantic and instance predictions, employing deep learning-based clustering to directly output instance IDs, enabling unified scene understanding with reduced processing times and resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional separate semantic and instance segmentation models are used, then each task can be processed independently, but the system complexity increases and processing time extends
Solution Approach 1:
The patent combines semantic segmentation and instance segmentation into a single unified neural network model that processes both tasks simultaneously. The network shares common feature extraction layers and uses a joint loss function to optimize both segmentation types together, eliminating the need for separate independent models and reducing overall system complexity while improving processing efficiency.
Solution Approach 2:
The unified neural network model performs multiple functions by simultaneously executing both semantic segmentation (classifying pixels into categories) and instance segmentation (distinguishing individual objects of the same class). This multi-functional approach allows a single system to handle diverse segmentation requirements without needing separate specialized models for each task.
2Reliability
If multiple separate segmentation models are deployed, then comprehensive scene understanding is achieved, but processing resources increase
Solution Approach 1:
The patent merges semantic and instance segmentation operations into a single neural network execution, sharing computational resources across both tasks. The unified model uses common convolutional layers and feature extraction mechanisms for both segmentation types, reducing redundant computations and lowering the overall processing resource requirements while maintaining comprehensive scene understanding capabilities.
Solution Approach 2:
The network architecture segments the processing task into distinct but integrated components: a shared feature extraction backbone that serves both segmentation types, and separate but coordinated output heads for semantic and instance predictions. This segmented yet unified approach optimizes resource usage by avoiding duplicate processing while preserving the specialized capabilities needed for accurate scene understanding.
3Measurement precision
If external grouping techniques are used for instance segmentation, then instance separation is achieved, but processing time increases
Solution Approach 1:
The neural network performs instance grouping and separation as a preliminary integrated operation during the main segmentation process, rather than as a subsequent external step. The model directly outputs instance masks and labels through its instance segmentation head, embedding the grouping capability within the primary processing pipeline and eliminating the need for separate post-processing grouping operations.
Solution Approach 2:
The patent combines the instance grouping function with the instance segmentation function in a single unified process. The neural network simultaneously performs pixel-level classification, instance boundary detection, and instance ID assignment in one integrated forward pass, eliminating the time delay associated with sequential external grouping techniques applied after initial segmentation.
Data Source
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AI summary
The present disclosure provides a system for panoptic segmentation of sensory information, comprising: a shared encoder for feature extraction; a first decoder and prediction module configured for semantic prediction of sensory information; a second decoder and prediction module configured for instance prediction of the sensory information, wherein the second decoder and prediction module is configured to provide the instance prediction by implementing deep learning-based clustering allowing the neural network to directly group portions of input into object instances; and a panoptic prediction module configured to provide a panoptic prediction based on the semantic prediction and the instance prediction provided by the first decoder and prediction module and the second decoder and prediction module, respectively.