Multi-Stream CNN Shape Fusion for Boundary Detection
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Solution Overview
Problem
Conventional computer vision approaches for tasks like autonomous driving and robotic control often lack the precision needed due to the dense processing of color, shape, and texture information within deep neural networks, which can lead to suboptimal object classification and segmentation.
Innovation Solution
A multi-stream CNN architecture is employed, where a primary stream processes image data for semantic segmentation, and a separate shape stream processes shape information in parallel, with a gating mechanism to connect intermediate layers and a fusion module to combine information for improved boundary detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If color, shape, and texture information are processed together inside a deep neural network, then the network can process all visual features, but the precision of object boundary detection and segmentation deteriorates because these parameters contain very different amounts of information relevant for recognition
Solution Approach 1:
The patent divides the visual processing into separate streams: a primary stream that processes color and texture information, and a shape stream that processes shape information. This segmentation allows each stream to specialize in processing specific types of visual information, improving boundary detection accuracy while maintaining comprehensive processing capability.
Solution Approach 2:
The patent extracts shape information as a separate processing stream, taking it out from the dense processing of all visual features. By isolating shape information, the network can give it appropriate weight and attention, preventing it from being overwhelmed by color and texture information that contain different amounts of relevant data for recognition.
2Device complexity
If a dense image representation is used where all information is processed together, then the network structure remains simple, but the level of precision needed for autonomous driving and robotic control applications is not achieved
Solution Approach 1:
The network is segmented into multiple functional streams with specialized purposes. The primary stream handles color and texture, while the shape stream handles shape information. This segmentation improves reliability for critical applications without significantly increasing overall network complexity, as each stream uses standard convolutional neural network components.
Solution Approach 2:
The patent adds a temporal dimension to the network architecture by introducing a shape stream that operates in parallel with the primary stream. This dimensional expansion allows the network to process shape information separately and fuse it with other visual features, improving classification accuracy while maintaining a modular and manageable structure.
3Measurement precision
If shape information is processed separately in a parallel stream, then boundary detection accuracy improves, but the device complexity increases due to multiple streams and gating mechanisms
Solution Approach 1:
The patent merges the primary stream and shape stream through a fusion module that combines their outputs. This merging allows the network to leverage the strengths of both streams while maintaining a unified architecture. The gating mechanism dynamically controls the fusion process, but the overall structure remains integrated rather than completely separate, balancing complexity and performance.
Solution Approach 2:
The gating mechanism acts as an intermediary between the primary stream and shape stream, dynamically controlling how shape information is integrated into the overall processing. This intermediary component manages the interaction between streams without requiring complete architectural redesign, thus improving boundary detection while controlling complexity through a localized control mechanism.
Data Source
AI summary
Various types of image analysis benefit from a multi-stream architecture that allows the analysis to consider shape data. A shape stream can process image data in parallel with a primary stream, where data from layers of a network in the primary stream is provided as input to a network of the shape stream. The shape data can be fused with the primary analysis data to produce more accurate output, such as to produce accurate boundary information when the shape data is used with semantic segmentation data produced by the primary stream. A gate structure can be used to connect the intermediate layers of the primary and shape streams, using higher level activations to gate lower level activations in the shape stream. Such a gate structure can help focus the shape stream on the relevant information and reduces any additional weight of the shape stream.