Multi-Task Vision Network for Real-Time 3D Instance Segmentation
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Solution Overview
Problem
Current computer vision techniques are inefficient for real-time applications due to high processing times and memory usage, making them unsuitable for applications like autonomous vehicles, where they require multiple neural networks and struggle to provide accurate object detection quickly.
Innovation Solution
A machine-learning (ML) architecture that jointly trains components to output multiple tasks such as region of interest, semantic segmentation, direction logits, and depth data in a single forward propagation pass, using a backbone component and sub-networks to improve processing speed and accuracy, and incorporates techniques like atrous convolutions and channel-wise pooling to reduce memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple neural networks are used to improve object detection accuracy, then measurement precision is improved, but device complexity and memory usage increase
Solution Approach 1:
The patent combines multiple neural network functions into a single multi-task learning framework that simultaneously performs region proposal, semantic segmentation, instance segmentation, and 3D bounding box detection. This merging approach maintains high detection accuracy while reducing the number of separate networks required, directly addressing the contradiction between accuracy and complexity.
Solution Approach 2:
The neural network architecture is designed with multi-functionality to handle multiple detection tasks simultaneously through a unified framework. The single network performs diverse functions including 2D/3D object detection, semantic segmentation, and instance segmentation, eliminating the need for multiple specialized networks while maintaining comprehensive detection capabilities.
2Measurement precision
If multiple neural networks are used to improve object detection accuracy, then measurement precision is improved, but processing time increases
Solution Approach 1:
By merging multiple detection tasks into a single unified neural network processing pipeline, the system eliminates the sequential processing time required for multiple separate networks. The multi-task learning framework processes region proposals, semantic segmentation, instance segmentation, and 3D bounding box detection simultaneously in a single forward pass, significantly reducing total processing time while maintaining accuracy.
3Measurement precision
If conventional computer vision techniques are used to improve object detection accuracy, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The patent employs parameter changes including atrous convolutions with different dilation rates to capture multi-scale object features efficiently. This approach enables the network to detect objects of varying sizes without requiring separate processing for each scale, improving both accuracy and processing speed to achieve real-time performance at 30 frames per second on consumer-grade hardware.
4Device complexity
If atrous convolutions and channel-wise pooling are used to reduce memory usage, then device complexity is reduced, but measurement precision may worsen
Solution Approach 1:
The patent applies channel-wise pooling that segments and processes feature channels independently, reducing memory requirements by eliminating redundant information across channels. This segmentation approach maintains essential feature representations needed for accurate detection while significantly reducing the memory footprint, thus resolving the contradiction between memory efficiency and detection accuracy.
Data Source
AI summary
A machine-learning (ML) architecture for determining three or more outputs, such as a two and/or three-dimensional region of interest, semantic segmentation, direction logits, depth data, and/or instance segmentation associated with an object in an image. The ML architecture may output these outputs at a rate of 30 or more frames per second on consumer grade hardware.