Capsule Network Object Detection for Vehicle Navigation
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Solution Overview
Problem
Existing vehicle navigation systems face challenges in accurately and timely acquiring environmental data for safe and efficient operation, particularly in autonomous and semi-autonomous modes, due to limitations in object detection and localization using traditional methods.
Innovation Solution
The use of a capsule network trained with a scale-invariant normalization function, such as Max−min( ), to process video camera data for detecting and locating objects, determining routing coefficients, and predicting object locations in global coordinates, which enables improved object recognition and vehicle path determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional object detection methods are used in vehicle navigation systems, then the system complexity remains manageable, but the accuracy and timeliness of environmental data acquisition deteriorates
Solution Approach 1:
The patent replaces traditional mechanical/object-based detection systems with a capsule network, a type of neural network that uses dynamic routing mechanisms. This substitution enables more accurate object detection and localization by learning hierarchical features from video camera data, directly addressing the need for improved measurement precision in environmental data acquisition.
Solution Approach 2:
The patent introduces scale-invariant normalization functions that dynamically adjust routing coefficients based on object scale and position. This parameter change allows the system to maintain high detection accuracy across varying distances and object sizes, resolving the contradiction between detection precision and system complexity by adapting parameters rather than increasing system规模.
2Loss of time
If traditional object detection methods are used, then the computational resources required are lower, but the timeliness of environmental data acquisition deteriorates
Solution Approach 1:
The capsule network performs preliminary feature extraction and hierarchical learning during the detection process, organizing information in advance through multiple processing layers. This preliminary action enables faster final object identification and localization, improving data acquisition timeliness while managing computational resources through structured processing.
Solution Approach 2:
The dynamic routing mechanism in the capsule network implements feedback loops where routing coefficients are continuously adjusted based on prediction errors and object characteristics. This feedback enables the system to adaptively optimize computational resource allocation, improving detection speed and timeliness while maintaining efficient resource usage.
3Reliability
If capsule networks with scale-invariant normalization are implemented, then object recognition accuracy improves, but the device complexity increases
Solution Approach 1:
The scale-invariant normalization function dynamically adjusts routing coefficients based on object scale, position, and other parameters. This parameter adaptation allows the network to maintain high recognition accuracy across diverse conditions without requiring a larger or more complex network architecture, effectively resolving the contradiction between reliability and device complexity.
Solution Approach 2:
The capsule network implements dynamic routing where connection weights and routing coefficients are continuously adjusted during inference based on input characteristics. This dynamic behavior enables the network to adapt to varying object scales and positions, improving recognition accuracy while maintaining a compact architecture through adaptive parameter adjustment rather than fixed complex structures.
Data Source
AI summary
A system, comprising a computer that includes a processor and a memory, the memory storing instructions executable by the processor to detect and locate an object by processing video camera data with a capsule network, wherein training the capsule network includes determining routing coefficients with a scale-invariant normalization function. The computer can be further programmed to receive the detected and located object.