Gated Dense Pyramid Blocks for Semantic Image Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current semantic image segmentation techniques using convolutional neural networks face inefficiencies in information flow between layers, limiting their effectiveness in predicting pixel-wise category labels and detecting multiple objects in images.
Innovation Solution
The implementation of gated dense pyramid blocks, which utilize dense connected networks to capture multi-scale feature maps and employ a reverse up-sampling network to recover high-resolution features, along with a gated weight function to balance feature importance, enhancing information flow and context integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If convolutional neural networks are used for semantic image segmentation, then pixel-wise category labels can be predicted, but information flow between layers is inefficient
Solution Approach 1:
The network is divided into multiple pyramid levels (first pyramid level, second pyramid level, etc.), each handling different scale features. This segmentation allows efficient information flow at multiple scales simultaneously, resolving the contradiction between information flow efficiency and network complexity.
Solution Approach 2:
The patent introduces a pyramid structure that adds a dimensional aspect of multi-scale processing. By organizing features across different pyramid levels, the network achieves efficient cross-layer information flow without proportionally increasing complexity, as the pyramid structure provides a systematic organization framework.
2Loss of information
If dense connected networks are used to capture multi-scale features, then context information is enriched, but computational complexity increases
Solution Approach 1:
The dense connected network is organized into segmented pyramid levels, where each level processes specific scale information. This segmentation enables context information to be captured and retained systematically across scales while managing computational complexity through hierarchical organization rather than a monolithic dense connection.
Solution Approach 2:
The pyramid structure implements a nested organization where lower pyramid levels process detailed features and higher levels capture contextual information. This nesting allows multi-scale context information to be integrated efficiently, with each level building upon the previous one in a contained manner that manages computational complexity.
3Measurement precision
If reverse up-sampling network is used to recover high-resolution features, then feature resolution is improved, but processing time increases
Solution Approach 1:
The reverse up-sampling network recovers high-resolution features from lower pyramid levels in a preliminary manner, preparing these features for subsequent processing stages. By performing this recovery early in the pipeline, the system optimizes feature resolution without causing delays in later processing steps, thus managing processing time effectively.
Data Source
AI summary
An example apparatus for semantic image segmentation includes a receiver to receive an image to be segmented. The apparatus also includes a gated dense pyramid network including a plurality of gated dense pyramid (GDP) blocks to be trained to generate semantic labels for respective pixels in the received image. The apparatus further includes a generator to generate a segmented image based on the generated semantic labels.