Parallel Header Perception Network for Real-Time Multi-Task Object Detection
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Solution Overview
Problem
Current visual perception networks require multiple networks to perform various detection tasks, leading to increased computational load, power consumption, and reduced real-time detection capabilities.
Innovation Solution
A perception network architecture featuring a backbone and multiple parallel headers, where each parallel header is connected to the backbone and independently performs detection tasks using RPN, ROI-ALIGN, and RCNN modules, sharing a common backbone for feature extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple separate networks are used to implement different detection tasks, then each task can be performed with dedicated optimization, but the calculation amount and power consumption increase dramatically
Solution Approach 1:
The patent merges multiple separate detection networks into a single unified network architecture that shares a common backbone for feature extraction. Multiple task-specific detection heads are integrated into this shared backbone, allowing the system to perform multiple detection tasks simultaneously while reducing redundant computations and lowering power consumption compared to running separate networks for each task.
Solution Approach 2:
The unified network architecture is designed with multi-functionality, where a single backbone serves multiple detection tasks through shared feature extraction. The network can simultaneously perform different types of object detection, semantic segmentation, and other vision tasks by routing features to appropriate task-specific heads, making the system universal rather than task-specific.
2Measurement precision
If multiple separate networks are used to implement different detection tasks, then each task can be performed with dedicated optimization, but the calculation amount increases
Solution Approach 1:
The patent combines multiple detection tasks into a single network framework where the backbone features are shared across all tasks. This merging eliminates redundant feature extraction computations that would occur if separate networks were used, significantly reducing the overall calculation amount while maintaining detection precision through task-specific detection heads.
Solution Approach 2:
The network is segmented into a shared backbone for common feature extraction and task-specific detection heads for individual tasks. This segmentation allows efficient resource utilization where the expensive feature extraction portion is shared, while only the lighter detection head computations are task-specific, optimizing the balance between precision and computational efficiency.
3Measurement precision
If multiple separate networks are used to implement different detection tasks, then each task can be performed with dedicated optimization, but the detection speed decreases making real-time detection difficult
Solution Approach 1:
The patent merges multiple detection tasks into a single unified network that processes input images once through a shared backbone, then branches to multiple task-specific detection heads. This approach performs all detection tasks in parallel from the same extracted features, achieving real-time detection speeds that would be impossible with sequential processing through multiple separate networks.
4Measurement precision
If multiple separate networks are used to implement different detection tasks, then each task can be performed with dedicated optimization, but the system complexity increases
Solution Approach 1:
The patent combines multiple detection tasks into a single unified network architecture, reducing system complexity by eliminating the need to manage, deploy, and coordinate multiple separate networks. The unified structure simplifies the system while maintaining detection precision through task-specific detection heads that share a common backbone.
Data Source
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AI summary
This application relates to the field of artificial intelligence, and specifically, to the field of computer vision, and discloses a perception network based on a plurality of headers. The perception network includes a backbone and the plurality of parallel headers. The plurality of parallel headers are connected to the backbone. The backbone is configured to receive an input image, perform convolution processing on the input image, and output feature maps, corresponding to the image, that have different resolutions. Each of the plurality of parallel headers is configured to detect a task object in a task based on the feature maps output by the backbone, and output a 2D box of a region in which the task object is located and confidence corresponding to each 2D box. Each parallel header detects a different task object. The task object is an object that needs to be detected in the task. Higher confidence indicates a higher probability that the task object corresponding to the task exists in the 2D box corresponding to the confidence.