Deep Learning Stopper-Edge Inspection for False Reject Reduction
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Solution Overview
Problem
Automated visual inspection systems for pharmaceutical products face challenges in accurately distinguishing particles from bubbles in high viscosity solutions, leading to high false reject rates and equipment redundancy due to poor performance at specific inspection stations.
Innovation Solution
Applying deep learning to an AVI station with a line scan camera to detect and distinguish objects on a vessel stopper edge, using a trained neural network to generate accurate two-dimensional images and reduce false rejects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning algorithms are used for defect detection, then the system is easier to implement, but the detection accuracy and reliability are insufficient
Solution Approach 1:
The patent replaces traditional machine learning algorithms (mechanical/system A) with deep learning algorithms (system B) that utilize neural networks. This substitution enables the system to achieve higher detection accuracy and reliability by automatically extracting features from images, while the modular architecture manages the increased computational complexity through standardized processing pipelines.
2Reliability
If deep learning algorithms are implemented, then detection accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-processing images (normalization, augmentation) and pre-training neural network models on large datasets before actual defect detection. This allows the model to learn general defect patterns in advance, reducing the computational burden during real-time inspection while maintaining high reliability.
Solution Approach 2:
The deep learning system is divided into modular components: image acquisition module, pre-processing module, neural network analysis module, and result output module. Each module handles specific computational tasks independently, allowing for optimized resource allocation and parallel processing that reduces overall computational resource consumption while maintaining detection reliability.
3Productivity
If automated visual inspection is implemented, then productivity increases, but the ability to detect subtle or varying defect types decreases
Solution Approach 1:
The system employs dynamic adaptability through configurable neural network architectures and adjustable detection parameters. The inspection system can dynamically adapt to different defect types by modifying network configurations, threshold values, and processing parameters, enabling high-speed automated inspection across multiple defect categories without sacrificing detection capability.
Solution Approach 2:
The patent implements a universal inspection platform that can detect multiple defect types (surface defects, dimensional deviations, contamination, etc.) using a single deep learning system. The neural network is trained on diverse defect patterns and can generalize to detect various defect types, providing both high productivity and broad adaptability through one multi-functional system.
Data Source
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AI summary
In a method for enhancing accuracy and efficiency in automated visual inspection of vessels, a vessel containing a sample is oriented such that a line scan camera has a profile view of an edge of a stopper of the vessel. A plurality of images of the edge of the stopper is captured by the first line scan camera while spinning the vessel, where each image of the plurality of images corresponds to a different rotational position of the vessel. A two-dimensional image of the edge of the stopper is generated based on at least the plurality of images, and pixels of the two-dimensional image are processed, by one or more processors executing an inference model that includes a trained neural network, to generate output data indicative of a likelihood that the sample is defective.