Image Processing for Visual Defect Detection Accuracy
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Solution Overview
Problem
Conventional methods face challenges in generating effective data related to defects in the appearance of workpieces, particularly in visual inspections, where capturing and processing image data to detect defects is inefficient.
Innovation Solution
A computer-readable storage medium with instructions that process original and captured image data using distinct image processes, including noise addition and blurring, to generate output data related to defects, utilizing a machine learning model trained on pre-processed data with added noise and blur, ensuring accurate defect detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image processing methods are used for defect detection, then the inspection process can be performed, but the accuracy of defect detection is insufficient
Solution Approach 1:
The patent applies preliminary action by pre-processing the captured image data through noise addition and blurring operations before comparing it with the original image data. This pre-processing simulates real-world imaging conditions and prepares the data for more accurate defect detection, thereby improving both detection accuracy and inspection reliability
Solution Approach 2:
The patent changes the parameters of the image data by adding noise and applying blurring effects to the captured image. These parameter modifications make the processed captured image more representative of actual inspection conditions, enabling more reliable defect detection while maintaining high accuracy
2Measurement precision
If distinct image processes are applied to original and captured image data, then the defect detection accuracy is improved, but the processing complexity increases
Solution Approach 1:
The patent segments the image processing into two distinct processes: one for processing the original image data and another for processing the captured image data. Each process applies specific operations (noise addition, blurring) tailored to the characteristics of the respective input, thereby improving defect detection accuracy while keeping each individual process relatively simple and manageable
3Reliability
If noise addition and blurring processes are used, then the processed image data better represents real inspection conditions, but the processing time increases
Solution Approach 1:
The patent applies partial action by implementing noise addition and blurring processes selectively - only on the captured image data that requires enhancement to match real inspection conditions. Not all image processing steps are applied uniformly, which reduces unnecessary processing time while still achieving the goal of improving inspection reliability through targeted pre-processing
Data Source
AI summary
A computer generates processed original image data by executing a first image process on original image data. The original image data represents an object image to be printed. The computer generates processed capture image data by executing a second image process on captured image data. The captured image data represents a captured object image. The captured object image is obtained by capturing an image of a printed object. The printed object is produced by printing the object image. The computer generates output data by executing a third image process on the processed original image data and the processed captured image data. The output data is related to a defect in an appearance of the printed object. The first image process includes a first process, which is not included in the second image process. The second image process includes a second process, which is not included in the first image process.