Electronic Defect Detection via Walsh-Hadamard Transform
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Solution Overview
Problem
Current vision-based surface inspection systems face challenges such as variability in image conditions, diverse defect appearances, lack of training samples, and the need for real-time high-accuracy defect detection without prior knowledge, making them unsuitable for automated applications.
Innovation Solution
The proposed system employs a combination of discrete transforms like the Walsh-Hadamard Transform and bi-thresholding for defect detection, capable of operating without prior knowledge and handling varying conditions, with a computational complexity of O[N Log(N)] to enable real-time processing, and includes a method for line detection using one-dimensional transforms in specific directions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection is used, then inspection accuracy is improved, but inspection productivity deteriorates and error rates increase
Solution Approach 1:
The patent replaces manual visual inspection with an automated electronic device that captures images and processes them through computational algorithms. The system uses image sensors to capture surface defects and digital processing units to analyze the images, eliminating the need for human inspectors while maintaining high detection accuracy and improving productivity through continuous automated operation.
Solution Approach 2:
The inspection system performs self-diagnosis and self-correction through automated image processing. The device captures images, processes them through defect detection algorithms, and generates reports without human intervention. The system continuously monitors and adjusts its own performance, eliminating dependency on inspector knowledge and attention while maintaining consistent accuracy.
2Productivity
If automated inspection is implemented, then inspection productivity is improved, but defect detection capability deteriorates due to lack of prior knowledge
Solution Approach 1:
The patent employs dynamic thresholding techniques where the system automatically adjusts detection parameters based on the analyzed images. The defect detection algorithm modifies its sensitivity and threshold values in real-time during image processing, allowing it to adapt to different defect types, surface conditions, and illumination variations without requiring pre-programmed knowledge of specific defect patterns.
Solution Approach 2:
The system incorporates feedback mechanisms where the processed images and detection results are continuously reviewed. The algorithm learns from the analysis of multiple images and adjusts its detection criteria accordingly. This feedback loop enables the automated system to improve its defect detection capability over time without human intervention, maintaining high accuracy while ensuring continuous productivity.
3Measurement precision
If complex image processing is applied, then defect detection accuracy is improved, but processing time increases
Solution Approach 1:
The patent divides the image processing into distinct sequential stages: image capture, preliminary processing, defect detection through algorithmic analysis, and result generation. Each stage handles specific aspects of the inspection task independently, allowing the system to process images efficiently through modular operations. This segmentation enables complex analysis to be performed in manageable steps without excessive time delay.
Solution Approach 2:
The system performs preliminary image processing actions such as noise filtering, contrast enhancement, and normalization before the main defect detection analysis. These preparatory steps optimize the image quality in advance, reducing the computational burden during the actual defect detection phase. By completing preliminary actions beforehand, the system achieves high detection accuracy while minimizing overall processing time.
Data Source
AI summary
An electronic device configured for defect detection is described. The electronic device includes a processor and instructions stored in memory that is in electronic communication with the processor. The electronic device performs background suppression on the image data based on a transform of the image data to obtain a score map. The electronic device also applies thresholding to the score map to generate a detection mask. The thresholding comprises bi-thresholding. The electronic device additionally detects any defects based on the detection mask. The electronic device further indicates any defects.