Semantic Segmentation Neural Network Edge Loss Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Semantic segmentation using deep neural networks faces challenges in accuracy when relying on coarse labels that lack edge information, leading to incorrect shape predictions of semantic objects, particularly in resource-intensive domains like medicine where fine labeling is costly and time-consuming.
Innovation Solution
A method that calculates and back-propagates both segmentation loss and edge loss using a neural network architecture that includes an edge detection neural network and an attention neural network, allowing for accurate shape prediction of semantic objects even with coarse labels, by extracting and refining edge information through image processing and attention mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If deep neural network is learned using coarse labels without edge information, then labeling cost is reduced, but segmentation accuracy and shape prediction accuracy deteriorate
Solution Approach 1:
The loss function is segmented into two distinct components: segmentation loss for classification accuracy and edge loss for shape accuracy. This allows the model to separately optimize for both objectives using coarse labels, achieving high segmentation accuracy without requiring expensive fine labels with edge information.
Solution Approach 2:
An edge detection module is introduced as an intermediary component that processes the segmentation output to generate edge information. This module uses image processing logic to extract edges from the segmentation map, providing shape constraints without requiring manually annotated edge labels.
2Loss of time
If deep neural network is learned using coarse labels, then labeling effort is reduced, but shape prediction accuracy deteriorates
Solution Approach 1:
The edge detection module provides feedback to the segmentation neural network by calculating edge loss based on the difference between detected edges and actual object boundaries. This feedback mechanism enables the model to learn accurate shape predictions from coarse labels through iterative optimization.
Solution Approach 2:
Image processing logic is applied preliminarily to generate edge information from the segmentation output before calculating the final loss. This preliminary edge extraction enables the model to incorporate shape constraints without requiring pre-annotated edge labels.
3Manufacturing precision
If edge loss is accurately calculated using edge detection neural network, then shape prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The edge detection functionality is extracted as a separate, lightweight module with fewer layers and parameters than the main segmentation network. This extraction allows accurate edge detection without significantly increasing overall computational complexity, as the edge module processes only the segmentation output rather than the full image.
Data Source
AI summary
A semantic segmentation method and apparatus for improving an accuracy of a segmentation result are provided. The semantic segmentation method inputs a labeled image into a segmentation neural network to obtain segmentation information for the image, and back-propagates a segmentation loss for the segmentation information to update the segmentation neural network. The segmentation neural network is updated by further back-propagating an edge loss for the segmentation information.