Instance-First Pseudo-Image Segmentation Under Compute Constraints
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Solution Overview
Problem
Autonomous vehicles face challenges in efficiently identifying and segmenting objects in complex environments due to computational resource constraints, which hinders their ability to quickly perceive surroundings and determine optimal paths.
Innovation Solution
A system is implemented that segments instances of objects in images using a pseudo-image approach, enabling faster object identification with reduced computational resources by employing a neural network to classify and group image sections, thereby enhancing the vehicle's perception capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional object segmentation methods are used in autonomous vehicles, then object identification accuracy can be maintained, but computational resource consumption increases and processing speed decreases
Solution Approach 1:
The patent applies segmentation by dividing the image processing task into two distinct stages: instance portion segmentation that identifies individual object parts, and semantic segmentation that classifies object types. This division allows the system to process and filter information hierarchically, reducing the overall computational burden while maintaining identification accuracy.
Solution Approach 2:
The patent introduces an intermediary mechanism by using instance portion segmentation results as a preliminary filter before performing full semantic segmentation. This intermediate step enables the system to focus computational resources only on relevant regions, thereby reducing energy consumption while preserving object identification speed and accuracy.
2Measurement precision
If complex environment perception is performed to ensure safety, then perception accuracy improves, but computational complexity and resource requirements increase
Solution Approach 1:
The patent segments the perception task into instance portion segmentation and semantic segmentation, allowing the system to achieve high perception accuracy through a structured two-stage process rather than a single complex operation, thereby managing computational complexity more effectively.
Solution Approach 2:
The patent implements partial action by performing instance portion segmentation on the entire image first, then using those results to guide semantic segmentation only in relevant areas. This approach maintains high perception accuracy while avoiding the computational complexity of applying full semantic segmentation uniformly across the entire image.
3Measurement precision
If full semantic segmentation is performed on all image regions, then object classification accuracy improves, but processing time increases
Solution Approach 1:
The patent applies preliminary action by performing instance portion segmentation before semantic segmentation. This preliminary step identifies and marks individual object portions, allowing the subsequent semantic segmentation to focus only on these identified regions, thereby maintaining classification accuracy while reducing overall processing time.
Solution Approach 2:
The patent uses partial action by limiting semantic segmentation to only those regions identified by instance portion segmentation, rather than applying it to the entire image. This selective approach preserves object classification accuracy while significantly reducing the time required for processing.
Data Source
AI summary
A system is configured to segment instances of objects in a pseudo-image. The pseudo-image can be generated from a 3D image coupled to a vehicle. The system can receive the pseudo-image, which can include multiple sections. The system can determine an object classification for a section and determine an instance portion classification for the section. The system can group the section with another section based on the object classification and the instance portion classification. The grouping can correspond to an instance of an object in the image. The system can use the grouping to navigate the vehicle.