A flexible design for a cost effective, high throughput inspection system for pharmaceutical capsules

Karloff, Anthony C.; Scott, N.E.; Muscedere, Roberto

doi:10.1109/icit.2008.4608671

Cited by 9 publications

(4 citation statements)

References 3 publications

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“…A computer vision based system controller requires one time establishment cost and possible maintenance costs. There are few capsule sorting machines with quality process control settings available in the market at a cost of over $500,000 per mach ine [1]. DJA-PHA RMA, a division of Daiichi Jitsugyo America has Capsules Visual Inspection System CVIS -SXX-E that inspects uni-color and bi-color hard gelatin capsules of size 1 through 5 and these with printed marks except both body and cap are transparent and dark color [2].…”

Section: State-of-the-art and Motivationmentioning

confidence: 99%

“…The system must be able to verify the capsule color and size including other defects satisfying the throughput at a rate of 1000 fu lly inspected capsules per minute. The system must also be flexib le enough to adjust for various sizes and colors of capsules, and accommodate future updates in image resolution and more thorough inspection algorith ms [1].…”

Section: Capsule Inspection System-optisortermentioning

confidence: 99%

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Computer Vision-Based Quality Inspection System of Transparent Gelatin Capsules in Pharmaceutical Applications

Islam¹,

Basalamah²,

Ahmadi³

et al. 2012

AJIS

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Real-time quality inspection of gelatin capsules in pharmaceutical applications is an important issue fro m the point of view of industry productivity and competitiveness. Computer vision-based automatic quality inspection is one of the solutions to this problem. Machine vision systems provide quality control and real-time feedback for industrial processes, overcoming physical limitations and subjective judgment of humans. In computer-vision based system a digital image obtained by a digital camera would usually have 24-bit color image. The analysis of an image with that many levels might require co mplicated image p rocessing techniques. But in real-time applicat ion, where a part has to be inspected within a few milliseconds, either we have to reduce the image to a more manageable nu mber of gray levels, usually two levels (binary image), and at the same time retain all necessary features of the original image. A b inary image can be obtained by thresholding the original image into two levels. In this paper, we have developed an image processing system using edge-based image segmentation techniques for quality inspection that satisfy the industrial requirements in pharmaceutical applicat ions to pass the accepted and rejected capsules.

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Section: State-of-the-art and Motivationmentioning

confidence: 99%

Section: Capsule Inspection System-optisortermentioning

confidence: 99%

Computer Vision-Based Quality Inspection System of Transparent Gelatin Capsules in Pharmaceutical Applications

Islam¹,

Basalamah²,

Ahmadi³

et al. 2012

AJIS

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“…Visual inspection of pharmaceutical products, an important and highly regulated task, represents such a real-world inspection domain, with rigorous accuracy and speed requirements. Herein, speed represents a crucial component, as automated visual inspection 1 – 3 of every single product is performed. The inspection processes hereby aim to avoid defective or substandard pharmaceutical products by detecting various defects that arise during the production process.…”

Section: Introductionmentioning

confidence: 99%

Coupling of unsupervised and supervised deep learning-based approaches for surface anomaly detection

Rački,

Tomaževič,

Skočaj

2024

J. Electron. Imag.

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Anomaly detection (AD) in an unsupervised manner has become the go-to approach in applications where data labeling proves problematic. However, these approaches are not completely unsupervised, since they rely on the weak knowledge of the dataset distribution into anomalous and anomaly-free subsets and typically require post-training threshold calibration in order to perform AD. Yet, they do not take advantage of available positive samples during training. In contrast, fully supervised approaches have proven to be more accurate and more efficient; however, they require a sufficient number of anomalous images to be labeled on a per-pixel level, which represents a labor-intensive task. In this article, we propose a hybrid approach that utilizes the best of both worlds. We use an unsupervised approach to build a model for generating pseudo labels, followed by a supervised approach to increase the robustness of AD. Moreover, we extend this approach with an active learning schema that results in learning with mixed supervision. We achieve several improvements, i.e., the utilization of available positive image samples, improved AD performance, and the retention of real-time performance. The proposed approach yields results that are comparable to the fully supervised approach, and at the very least, reduces the number of required labeled anomalous samples.

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“…In case of pharmaceutical capsules, various defects can occur in the process of production, such as color defect, dent, or crack, which may lead to decrease in company profit, increase in production time and unit cost, and uncertainty in the quality of product in the market, so the development of quality controller that processes quickly at low cost strengthens competitiveness [1]. Fig.…”

Section: ⅰ Introductionmentioning

confidence: 99%

Performance evaluation of Edge-based Method for classification of Gelatin Capsules

Kwon¹,

Choi²

2017

Journal of Digital Contents Society

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A flexible design for a cost effective, high throughput inspection system for pharmaceutical capsules

Cited by 9 publications

References 3 publications

Computer Vision-Based Quality Inspection System of Transparent Gelatin Capsules in Pharmaceutical Applications

Computer Vision-Based Quality Inspection System of Transparent Gelatin Capsules in Pharmaceutical Applications

Coupling of unsupervised and supervised deep learning-based approaches for surface anomaly detection

Performance evaluation of Edge-based Method for classification of Gelatin Capsules

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