Engineering Drawing Information Extraction Using R-CNN and Reinforcement Learning
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Solution Overview
Problem
Existing methods for extracting information from engineering drawings are labor-intensive and prone to errors due to difficulties in recognizing differences in orientation, scale, and unfamiliar symbols, as well as a lack of domain information, leading to inaccuracies in identifying nodes, edges, and annotations.
Innovation Solution
A system and method utilizing region-based convolutional neural networks (R-CNNs) for node classification and edge detection, combined with reinforcement learning for error correction, and an attribute database for domain information, to extract and validate attributes from engineering drawings with improved accuracy and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual methods are used for extracting information from engineering drawings, then flexibility and adaptability are maintained, but labor intensity increases and error rates rise due to difficulties in recognizing differences in orientation, scale, and unfamiliar symbols
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated computer-based system that uses image processing and machine learning algorithms to classify nodes, identify edges, and extract attributes from engineering drawings, thereby improving reliability while achieving substantial automation
Solution Approach 2:
The system transforms the drawing analysis problem by changing parameters such as orientation, scale, and symbol representation into standardized forms that can be automatically recognized and classified by the computer system, resolving the issue of recognizing differences in orientation and scale
2Productivity
If automated methods are used for extracting information from engineering drawings, then productivity increases and manual intervention is reduced, but accuracy decreases due to lack of domain information and inability to handle unfamiliar symbols
Solution Approach 1:
The system performs preliminary classification of nodes and identification of edges before extracting attributes, using pre-trained machine learning models and domain knowledge databases to prepare the drawing data in advance, which improves both productivity and precision by structuring the analysis process
Solution Approach 2:
The patent introduces an intermediary component that uses domain information and attribute databases to bridge the gap between automated image processing and accurate interpretation of engineering symbols, enabling the system to handle unfamiliar symbols and improve measurement precision
3Speed
If simple edge detection is used, then processing speed is maintained, but accuracy of edge identification decreases in complex drawings with multiple orientations and scales
Solution Approach 1:
The patent segments the edge detection process into multiple stages: initial edge detection, reinforcement learning-based refinement, and final validation against domain knowledge, which maintains processing speed while improving accuracy in complex drawings
Data Source
AI summary
A method and system for extracting information from a drawing. The method includes classifying nodes in the drawing, extracting attributes from the nodes, determining whether there are errors in the node attributes, and removing the nodes from the drawing. The method also includes identifying edges in the drawing, extracting attributes from the edges, and determining whether there are errors in the edge attributes. The system includes at least one processing component, at least one memory component, an identification component, an extraction component, and a correction component. The identification component is configured to classify nodes in the drawing, remove the nodes from the drawing, and identify edges in the drawing. The extraction component is configured to extract attributes from the nodes and edges. The correction component is configured to determine whether there are errors in the extracted attributes.