Multi-label Semantic Edge Detection Neural Network
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Solution Overview
Problem
Current computer vision methods for semantic boundary detection are inefficient due to the requirement of multiple neural networks and incorrect assumptions about pixel classification, which restricts the training and testing of neural networks, especially when edge pixels can belong to multiple semantic classes.
Innovation Solution
Transforming multi-class segmentation into multi-label segmentation, allowing pixels to belong to one or multiple semantic classes, and using a neural network to generate a multi-label edge-map by assigning multiple semantic labels to edge pixels, with supervision only in upper layers to leverage contextual information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple neural networks are used to combine semantic segmentation and edge detection, then detection capability is improved, but system complexity increases
Solution Approach 1:
The patent merges semantic segmentation and edge detection into a single unified neural network architecture. The network simultaneously performs both tasks by sharing convolutional layers and using a common feature extraction backbone, eliminating the need for separate networks while maintaining detection accuracy through multi-task learning objectives.
Solution Approach 2:
The unified neural network is designed to perform multiple functions: semantic segmentation, edge detection, and boundary classification. By making the network universal and capable of handling multiple detection tasks within a single architecture, the system reduces overall complexity while preserving the benefits of combined detection approaches.
2Ease of operation
If multi-class segmentation is used where each pixel belongs to only one class, then classification simplicity is improved, but boundary detection accuracy deteriorates
Solution Approach 1:
The patent segments the classification problem into two distinct parts: semantic class classification (multi-class) and boundary detection (binary classification). By separating these functions and allowing pixels to have both a primary semantic class and a boundary label, the system maintains classification simplicity while significantly improving boundary detection accuracy through dedicated boundary detection pathways.
Solution Approach 2:
The patent adds an additional classification dimension by introducing boundary detection as a separate classification task alongside semantic segmentation. Instead of forcing pixels into a single class category, the system adds a binary boundary/non-boundary dimension, allowing pixels to be classified as both semantic classes and boundary pixels simultaneously, thereby improving overall detection accuracy.
Data Source
AI summary
A image processing system for multi-label semantic edge detection in an image includes an image interface to receive an image of a scene including at least one object, a memory to store a neural network trained for performing a multi-label edge classification of input images assigning each pixel of edges of objects in the input images into one or multiple semantic classes, a processor to transform the image into a multi-label edge-map using the neural network detecting an edge of the object in the image and assigning multiple semantic labels to at least some pixels forming the edge, and an output interface to render the multi-label edge-map.