PCB Component Detection via Shadow and Super-Pixel Segmentation
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Solution Overview
Problem
Current methods for automated detection of printed circuit board (PCB) components are inefficient, particularly in distinguishing between components and shadows, which hinders accurate generation of bills of materials and refinement of manufacturing processes.
Innovation Solution
The implementation of shadow detection segmentation and super-pixel segmentation techniques using non-direct-lighting and direct-lighting images to identify PCB components, where shadow detection validates component presence based on shadow analysis and super-pixel segmentation clusters pixels by features to enhance detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional automated detection methods are used, then the detection process is simple, but the accuracy of distinguishing components from shadows is poor
Solution Approach 1:
The patent divides the detection process into two independent segmentation methods: shadow detection segmentation and super-pixel segmentation. Each method processes images independently to identify components, allowing the system to leverage the strengths of both approaches while maintaining modular complexity management.
Solution Approach 2:
The patent introduces a new dimension by capturing images under two different lighting conditions (direct-lighting and non-direct-lighting). This multi-dimensional approach allows the system to distinguish components from shadows by comparing how they appear under different illumination angles, thereby improving detection accuracy without significantly increasing operational complexity.
2Measurement precision
If shadow detection segmentation is performed using multiple lighting images, then component detection accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary shadow detection by comparing direct-lighting and non-direct-lighting images to identify shadow regions before conducting super-pixel segmentation. This preliminary action allows the system to focus computational resources only on non-shadow regions, reducing overall processing time while maintaining high detection accuracy.
Solution Approach 2:
The patent applies shadow detection segmentation to all images but then uses the results to guide partial processing in subsequent super-pixel segmentation. By performing excessive shadow detection initially and then using those results to reduce the scope of subsequent processing, the system achieves high accuracy while managing processing time effectively.
3Measurement precision
If super-pixel segmentation is used to cluster pixels by features, then detection accuracy improves, but computational complexity increases
Solution Approach 1:
The patent implements super-pixel segmentation that groups adjacent pixels with similar features into super-pixels, reducing the number of individual pixels that need to be processed. This segmentation approach maintains detection accuracy by preserving feature boundaries while significantly reducing computational complexity through data aggregation.
Solution Approach 2:
The patent merges adjacent pixels with similar characteristics into super-pixels, combining their features into unified representations. This merging process reduces the dimensionality of the data while preserving important boundary information, thereby improving computational efficiency without sacrificing detection accuracy.
4Measurement precision
If both shadow detection and super-pixel segmentation are performed, then component detection accuracy is maximized, but the detection process becomes more complex
Solution Approach 1:
The patent divides the overall detection system into two independent but complementary segmentation modules: shadow detection segmentation and super-pixel segmentation. Each module operates with its own algorithm and processing flow, allowing for independent optimization and simplification of each component while achieving maximum overall accuracy through their combination.
Solution Approach 2:
The patent uses shadow detection results as an intermediary that guides the subsequent super-pixel segmentation process. By using shadow masks as intermediate data structures, the system can reduce the scope of super-pixel processing to only non-shadow regions, thereby managing overall system complexity while maintaining maximum detection accuracy.
Data Source
AI summary
There is a need for more effective and efficient printed circuit board (PCB) design. This need can be addressed by, for example, solutions for performing automated PCB component estimation. In one example, a method includes identifying a plurality of initial component estimations for the PCB; performing a shadow detection segmentation using the plurality of initial component estimations, a non-direct-lighting image, and one or more direct-lighting images to generate a first set of detected PCB components; performing a super-pixel segmentation using the plurality of initial component estimations and the non-direct-lighting-image to generate a second set of detected PCB components; and generating a bill of materials for the PCB based at least in part on the first set of detected PCB components and the second set of detected PCB components.