Circuit Board Image Analysis for Automated Graph Extraction
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Solution Overview
Problem
Circuit board reverse engineering is a time-consuming and imprecise process, particularly when dealing with unknown or incomplete origins, and conventional methods lack the ability to autonomously unify graphical representations of circuit boards with digital design files for meaningful computational analysis.
Innovation Solution
An application that analyzes graphical representations of circuit boards to identify components and interconnections, converting them into a normalized graph format suitable for comparison and storage, enabling computational intelligence and automated analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual techniques are used for traversing and comparing available design specifications, then circuit board identification can be performed, but the process becomes time-consuming and tedious
Solution Approach 1:
The patent replaces manual mechanical inspection and comparison techniques with an automated computer vision system that uses image processing algorithms to detect, extract, and analyze circuit board features. The system automatically compares extracted features against a database of known circuit boards, eliminating the need for manual traversal and comparison of design specifications.
Solution Approach 2:
The patent introduces an intermediate computational layer that processes circuit board images through multiple stages: image pre-processing, feature detection, feature extraction, and database comparison. This intermediary system acts as a mediator between the raw image data and the final identification result, enabling automated analysis while maintaining high precision.
2Reliability
If conventional approaches are used for circuit board identification, then analysis can be performed, but the process lacks autonomy and requires manual intervention
Solution Approach 1:
The patent implements a self-service automated system that performs the entire circuit board identification process without human intervention. The system automatically captures images, processes them through image analysis algorithms, extracts relevant features, compares them against the database, and generates identification results. This self-service capability eliminates the need for manual analysis while maintaining reliable identification.
Solution Approach 2:
The patent replaces manual analytical processes with automated computational intelligence, including machine learning algorithms and pattern recognition systems. These automated systems perform feature detection, extraction, and comparison tasks that previously required human expertise, thereby increasing both automation level and reliability through consistent, error-free execution.
3Ease of operation
If digital design files are converted to schematics for comparison, then circuit analysis can be performed, but the process remains labor intensive and prohibits meaningful computational intelligence
Solution Approach 1:
The patent replaces the manual conversion process from digital design files to schematics with direct image processing of circuit board photographs or existing schematics. The system uses computer vision algorithms to detect and interpret circuit features directly from images, bypassing the need for manual file conversion and enabling automated computational analysis at high speed.
Solution Approach 2:
The patent introduces an intermediary image processing system that directly analyzes circuit board images or schematic images without requiring conversion to intermediate formats. This intermediary layer extracts features directly from the image data, enabling both ease of operation and high productivity through automated processing pipelines that can handle multiple circuits simultaneously.
Data Source
AI summary
A circuit analysis application receives an imaged representation of a circuit, such as a schematic, circuit board image, scan result, or other similar electronic pixelated representation capable of raster (video screen renderable) visualization. The application iterates through a sequence of extraction and classification operations, including an object or component extraction for identifying discrete components of the circuit, traces denoting connections between the components, and component identification for determining a type, and hence the electrical properties, of each component and the connections to other components. The result is a graph of circuit components and connections to other components, normalized in a form suitable for comparison to other circuit representations and stored in a database for subsequent comparison and identification with other unknown circuit forms.