Vision Inspection System Using Adaptive Histogram Equalization
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Solution Overview
Problem
Conventional vision inspection systems are labor-intensive, costly, and prone to human error due to low detection accuracy and image quality issues, leading to poor product consistency and slow throughput.
Innovation Solution
A vision inspection system that includes a sorting platform, an imaging device, and a vision inspection controller with an image histogram tool for contrast enhancement and an artificial intelligence learning module to customize image analysis, enabling efficient and accurate inspection of parts by redistributing lightness values and processing images based on an image analysis model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection is used, then labor cost is high and detection accuracy is low, but the system is simple to implement
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated machine vision system that uses imaging devices, histogram equalization processing, and AI algorithms to detect defects. This substitution eliminates human labor while achieving high detection accuracy through computational image enhancement and analysis.
Solution Approach 2:
The system performs self-service through automated image capture, automatic histogram equalization processing, and AI-based defect detection. The machine vision system independently completes the entire inspection process without human intervention, from imaging to defect identification and classification.
2Productivity
If machine vision inspection is used, then throughput increases, but image quality is poor due to poor contrast and brightness
Solution Approach 1:
The system performs preliminary histogram equalization processing on images before they are analyzed for defects. This pre-processing step enhances image contrast and brightness, ensuring that subsequent AI analysis operates on high-quality images, thereby maintaining both throughput and detection accuracy.
Solution Approach 2:
The patent introduces histogram equalization as an intermediary processing step between image capture and defect analysis. This intermediary technique transforms raw images into enhanced images with improved contrast, serving as a bridge that preserves throughput while significantly improving image quality for detection.
3Measurement precision
If multiple imaging at different brightness levels is used, then image quality improves, but throughput decreases
Solution Approach 1:
Instead of capturing multiple images at different brightness levels, the patent changes the parameter approach by applying histogram equalization to a single image. This parameter transformation technique redistributes pixel intensity values to enhance contrast, achieving improved image quality without requiring multiple captures, thus maintaining high throughput.
Solution Approach 2:
The patent replaces the mechanical approach of multiple physical image captures with a computational image processing method. By substituting repeated imaging with a single capture followed by histogram equalization algorithms, the system achieves similar or superior image quality while dramatically improving inspection throughput.
4Measurement precision
If conventional image analysis is used, then processing is fast, but detection accuracy is low leading to human error
Solution Approach 1:
The system employs AI algorithms that perform self-learning and adaptive detection. The AI model automatically identifies defect patterns, learns from training data, and makes detection decisions without human intervention, eliminating human errors while maintaining fast processing speeds through optimized computational algorithms.
Solution Approach 2:
The patent incorporates feedback mechanisms where the AI system continuously learns from inspection results and adjusts its detection parameters. This feedback loop improves detection accuracy over time by refining the AI model based on actual defect patterns observed during operation, while the system maintains efficient processing through optimized algorithms.
Data Source
AI summary
A vision inspection system includes a sorting platform having an upper surface supporting parts for inspection. An inspection station is positioned adjacent the sorting platform including an imaging device to image the parts in a field of view. A vision inspection controller receives images from the imaging device. The vision inspection controller includes an image histogram tool to pre-process the images to improve contrast of the images by redistributing lightness values of the images based on adaptive histogram equalization processing to generate enhanced images. The vision inspection controller processes the enhanced images based on an image analysis model to determine inspection results for each of the parts. The vision inspection controller has an artificial intelligence learning module operated to customize and configure the image analysis model based on the enhanced images.