Multi-Task Neural 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 reduce processing time and increase accuracy, allowing for real-time object detection on consumer-grade hardware.
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 unified neural network architecture. The network integrates feature extraction, object detection, and classification capabilities in one model, eliminating the need for separate neural networks while maintaining detection accuracy through shared feature representations and multi-task learning mechanisms.
Solution Approach 2:
The neural network is designed with multi-functionality to perform multiple tasks simultaneously - feature extraction, object detection, classification, and instance segmentation. This universal architecture processes different types of data and performs various detection functions within a single model, reducing overall system complexity while improving efficiency.
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 functions into a single neural network, the patent enables simultaneous processing of multiple tasks in one forward pass. This consolidation eliminates the sequential processing time required when using separate networks, reducing overall processing time while maintaining the accuracy benefits of multiple detection capabilities.
Solution Approach 2:
The unified neural network architecture enables continuous processing of detection tasks without interruption or sequential delays. The network processes features, detections, and classifications in an integrated continuous flow, maximizing computational efficiency and minimizing processing time compared to discrete multi-network approaches.
3Productivity
If a single ML model outputs multiple tasks in one forward propagation pass, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent segments the unified neural network into distinct functional modules - feature extraction layers, detection heads, classification modules, and segmentation components. Each module handles specific tasks, and their coordinated integration enables multi-task processing in one forward pass while keeping the overall architecture manageable and implementable on consumer hardware.
4Measurement precision
If joint training of multiple components is performed to improve accuracy, then measurement precision is improved, but loss of time during training increases
Solution Approach 1:
The patent implements joint training by merging multiple loss functions into a unified training objective. The network optimizes detection accuracy, classification precision, and segmentation quality simultaneously through a combined loss function, achieving coordinated improvement across all tasks in a single training process rather than separate sequential training runs.
Data Source
AI summary
Training a machine-learning (ML) architecture to determine three or more outputs at a rate of 30 or more frames per second on consumer grade hardware may comprise jointly training components of the ML using loss(es) determined across the components and/or consistency losses determined between outputs of two or more components. The ML architecture discussed herein may comprise one or more sets of neural network layers and/or respective components for determining 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.