Panoptic Segmentation DNN for Occluded Road Scene Instances
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional autonomous vehicle perception systems struggle with heavy occlusions, complex scenes, and limited accuracy in detecting and classifying animate objects, leading to unreliable object detection and slow performance.
Innovation Solution
A deep neural network (DNN) performs panoptic segmentation using a single pass to execute pixel-level class and instance segmentation, incorporating a common trunk and multiple heads to predict different outputs, including class confidence, instance regression, and depth, enabling accurate detection and classification of animate objects and environmental parts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple separate DNNs are used for different detection tasks, then detection coverage is improved, but system complexity and processing time increase
Solution Approach 1:
The patent combines multiple detection tasks (object detection, instance segmentation, panoptic segmentation) into a single unified DNN architecture. This single model integrates multiple heads that simultaneously perform different detection functions, reducing system complexity while maintaining comprehensive detection coverage through shared feature extraction and coordinated multi-task learning
2Reliability
If multiple separate DNNs are used for different detection tasks, then detection coverage is improved, but processing speed decreases
Solution Approach 1:
The unified DNN processes all detection tasks in parallel through a single forward pass, eliminating the sequential processing delays inherent in multiple separate models. The shared backbone and coordinated multi-head architecture enable simultaneous extraction of features for objects, instances, and panoptic segmentation, significantly improving processing speed while maintaining comprehensive detection coverage
3Ease of manufacture
If conventional boundary regressors are used, then implementation simplicity is maintained, but accuracy in complex scenes deteriorates
Solution Approach 1:
The patent segments the detection task into multiple specialized heads within the DNN: object detection head for bounding boxes, instance segmentation head for instance masks, and panoptic segmentation head for comprehensive scene understanding. This segmentation of functions within the unified architecture enables high accuracy in complex scenes by dedicating specific processing pathways to different detection objectives while maintaining overall system coherence
4Adaptability or versatility
If individual DNN parts are trained separately, then training flexibility is improved, but overall accuracy deteriorates
Solution Approach 1:
The unified DNN employs joint multi-task training where all detection heads are trained simultaneously with a combined loss function that balances objectives across object detection, instance segmentation, and panoptic segmentation. This joint training approach enables the model to learn shared features and task-specific features together, improving overall prediction accuracy while maintaining training flexibility through configurable loss weights and task priorities
Data Source
AI summary
A deep neural network(s) (DNN) may be used to perform panoptic segmentation by performing pixel-level class and instance segmentation of a scene using a single pass of the DNN. Generally, one or more images and/or other sensor data may be stitched together, stacked, and/or combined, and fed into a DNN that includes a common trunk and several heads that predict different outputs. The DNN may include a class confidence head that predicts a confidence map representing pixels that belong to particular classes, an instance regression head that predicts object instance data for detected objects, an instance clustering head that predicts a confidence map of pixels that belong to particular instances, and/or a depth head that predicts range values. These outputs may be decoded to identify bounding shapes, class labels, instance labels, and/or range values for detected objects, and used to enable safe path planning and control of an autonomous vehicle.