Annular Face Estimation via Multi-Model Segmentation
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Solution Overview
Problem
Existing methods for segmenting cross-sectional images of cylindrical shapes, such as those using circle or ellipse fitting and machine learning, often result in errors due to shape limitations and interruptions, leading to inaccuracies in estimating the thickness and presence of holes in annular faces.
Innovation Solution
An estimation processing program and apparatus that utilize multiple neural networks to segment annular, outer, and inner circumferences of cylindrical shapes, combining their outputs to improve accuracy and reduce errors through noise removal and arithmetic operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If circle or ellipse fitting method is used for segmentation, then the segmentation process is simple, but the shape of cross section is limited to circle or ellipse and inner and outer circles may cross
Solution Approach 1:
The patent segments the segmentation task into multiple independent neural networks, each responsible for specific boundaries (outer circumference, inner circumference). This allows the system to handle complex annular shapes while maintaining manageable processing through modular architecture.
Solution Approach 2:
The neural network-based segmentation system provides universal applicability to various cross-sectional shapes including circles, ellipses, and irregular annular shapes, replacing the shape-limited fitting method with a versatile deep learning approach.
2Adaptability or versatility
If machine learning method is used for edging learning, then segmentation can be performed, but edging region is liable to be interrupted and cylindrical shape prior knowledge cannot be utilized
Solution Approach 1:
The patent applies different processing strategies to different regions: the outer circumference network handles the outer boundary while the inner circumference network handles the inner boundary. This localized approach ensures continuous edging in each region while maintaining overall segmentation accuracy.
Solution Approach 2:
The system performs preliminary segmentation using multiple specialized neural networks before final combination. Each network is trained specifically for its designated boundary, ensuring continuous and accurate edging detection while incorporating cylindrical shape constraints through the multi-network architecture.
3Productivity
If single network segmentation is used, then processing is efficient, but error decision occurs about thickness and hole presence
Solution Approach 1:
The patent divides the segmentation task into separate neural networks for outer circumference and inner circumference detection. This segmentation allows each network to specialize in specific boundary detection, improving measurement precision for thickness and hole presence while maintaining processing efficiency through parallel operation.
Solution Approach 2:
The patent introduces an arithmetic operation unit as an intermediary that combines the outputs of multiple neural networks. This intermediary component synthesizes the segmented results to produce accurate thickness measurements and hole presence detection, resolving the contradiction between efficient processing and precise measurement.
Data Source
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AI summary
A method includes: inputting an image of a target to each of a first model for estimating a region of an annular face of the target, a second model for estimating a region in an outer circumference of the annular face and a third model for estimating a region in an inner circumference of the annular face, and outputting a region image, as an estimated region of the annular face, the region image being obtained by synthesizing first, second, and third elements. The first element is a region outputted as a result of the first model for calculating a sum set in the synthesizing, the second element is a region outputted as a result of the second model for calculating a sum set in the synthesizing, and the third element is a region outputted as a result of the third model for calculating a difference set in the synthesizing.