Wire Segmentation Using Two-Stage Neural Network
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Solution Overview
Problem
Existing image retouching methods require manual segmentation of wires and wire-like objects, which is time-consuming and prone to errors, especially when dealing with high-resolution images and complex wire patterns.
Innovation Solution
A digital design system utilizing a two-stage encoder-decoder network pipeline for wire segmentation, where the first stage captures global contextual information and the second stage refines wire detection at the original image resolution, leveraging machine learning to automate the process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual segmentation is used for wire removal, then flexibility and control are maintained, but time consumption and error rates increase significantly
Solution Approach 1:
The patent replaces manual mechanical segmentation with an automated machine learning system. The encoder-decoder network automatically identifies and segments wire regions in images, substituting human manual operations with an intelligent computational system that provides both speed and accuracy.
Solution Approach 2:
The system enables self-service automation where the machine learning model independently performs wire segmentation without human intervention. The model processes images autonomously, identifying wire regions and generating segmentation masks without requiring manual input or control.
2Measurement precision
If full high-resolution images are processed directly, then segmentation detail is improved, but computational resources and processing time increase
Solution Approach 1:
The patent applies segmentation at multiple levels: first dividing the high-resolution image into smaller patches for efficient processing, then using a two-stage encoder-decoder architecture that processes information at different resolutions. This hierarchical segmentation allows detailed wire pixel detection while reducing overall computational burden.
Solution Approach 2:
The system processes images at multiple resolution dimensions. The encoder-decoder network operates at both downsampled and full resolution levels, allowing the model to capture global context at lower resolutions and refine details at full resolution, optimizing the balance between accuracy and computational efficiency.
3Reliability
If complex wire patterns are segmented manually, then accuracy can be maintained through careful inspection, but time consumption increases exponentially
Solution Approach 1:
The patent replaces manual inspection and segmentation of complex wire patterns with an automated deep learning system. The encoder-decoder network is trained to reliably identify various wire patterns, substituting human visual inspection with an intelligent system that maintains accuracy while dramatically increasing throughput.
Solution Approach 2:
The system performs preliminary processing by downsampling images and processing patches before final high-resolution segmentation. This preliminary action prepares the data in advance, allowing the main segmentation process to focus on detailed wire pattern recognition, thereby improving both reliability and efficiency.
4Productivity
If automated machine learning methods are used, then processing speed and efficiency are improved, but system complexity increases
Solution Approach 1:
The patent divides the complex segmentation task into manageable components using a two-stage encoder-decoder architecture. The first stage processes downsampled images for coarse segmentation, while the second stage refines details at full resolution. This segmentation of the processing pipeline makes the complex system more manageable and efficient.
Solution Approach 2:
The system manages complexity by operating at multiple resolution dimensions. The encoder-decoder network processes information at both reduced and full resolution levels, allowing complex wire pattern recognition to be distributed across different processing stages and resolutions, thereby managing system complexity while maintaining high productivity.
Data Source
AI summary
Embodiments are disclosed for performing wire segmentation of images using machine learning. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an input image, generating, by a first trained neural network model, a global probability map representation of the input image indicating a probability value of each pixel including a representation of wires, and identifying regions of the input image indicated as including the representation of wires. The disclosed systems and methods further comprise, for each region from the identified regions, concatenating the region and information from the global probability map to create a concatenated input, and generating, by a second trained neural network model, a local probability map representation of the region based on the concatenated input, indicating pixels of the region including representations of wires. The disclosed systems and methods further comprise aggregating local probability maps for each region.