Computer Vision Quality Control for Automated Contaminant Detection
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Solution Overview
Problem
Existing quality control processes in industries such as pharmaceuticals, biotechnology, and food are manually intensive and prone to inaccuracies, often failing to detect unacceptable levels of contaminants.
Innovation Solution
An AI-based quality control system using computer visioning and deep learning, employing a residual network and convolutional neural networks to automatically identify contaminants in product samples, issuing alerts based on the type and level of contamination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual quality control processes are used, then operational simplicity is maintained, but detection accuracy and reliability deteriorate
Solution Approach 1:
The patent replaces manual visual inspection with an automated computer vision system using cameras and image processing algorithms. The mechanical/electronic imaging system captures product images, and computational algorithms automatically detect contaminants, eliminating the need for human operators while significantly improving detection accuracy and consistency.
Solution Approach 2:
The patent introduces an intermediary processing layer between image capture and quality determination. A multi-stage algorithm processes images through feature extraction, contamination detection, and classification modules, acting as a mediator that translates visual data into actionable quality control decisions with high precision.
2Productivity
If manual quality control processes are used, then implementation simplicity is maintained, but productivity and throughput deteriorate
Solution Approach 1:
The patent implements continuous automated image capture and processing as products move through the conveyor system. The camera continuously captures images at multiple stages, and the processing system operates without interruption, enabling high-throughput quality control that maintains productivity while the automated nature eliminates labor bottlenecks.
Solution Approach 2:
The system performs self-service quality control by automatically detecting and classifying contaminants without human intervention. The automated algorithm processes images, identifies contamination types, and triggers appropriate responses, allowing the system to monitor itself and maintain high productivity levels independently.
3Reliability
If manual quality control processes are used, then operational simplicity is maintained, but reliability and consistency deteriorate
Solution Approach 1:
The patent replaces the variability of manual inspection with deterministic automated image processing. The computer vision system uses standardized algorithms that consistently apply the same detection criteria, eliminating human variability and ensuring reliable, repeatable quality control results across different operators and shifts.
Solution Approach 2:
The system incorporates feedback mechanisms where detected contaminants and their locations are fed back into the processing system to refine detection algorithms. This continuous feedback loop improves the reliability of the system over time by learning from detected patterns and adjusting detection thresholds to maintain consistent high-quality control.
Data Source
AI summary
Implementations include receiving sample data, the sample data being generated as digital data representative of a sample of the product, providing a set of features by processing the sample data through multiple layers of a residual network, a first layer of the residual network identifying one or more features of the sample data, and a second layer of the residual network receiving the one or more features of the first layer, and identifying one or more additional features, processing the set of features using a CNN to identify a set of regions, and at least one object in a region of the set of regions, and determine a type of the at least one object, and selectively issuing an alert at least partially based on the type of the at least one object, the alert indicating contamination within the sample of the product.