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Updated: Jun 23, 2026

Using Synchrotron Radiation Microtomography to Investigate Multi-scale Three-dimensional Microelectronic Packages

Using Synchrotron Radiation Microtomography to Investigate Multi-scale Three-dimensional Microelectronic Packages

Published on: April 13, 2016

Real-time visual intelligence for defect detection in pharmaceutical packaging.

Ajantha Vijayakumar¹, Subramaniyaswamy Vairavasundaram², Joseph Abraham Sundar Koilraj¹

¹School of Computing, SASTRA Deemed University, Thanjavur, 613401, India.

Scientific Reports

|August 13, 2024

Summary

This summary is machine-generated.

This study introduces CBS-YOLOv8, an enhanced YOLOv8 model for pharmaceutical defect detection. It significantly improves accuracy and speed in identifying tablet defects, optimizing manufacturing quality assurance.

Keywords:

Computer vision Coordinate attention Defect detection Object detection YOLOv8

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Area of Science:

Computer Vision
Machine Learning
Industrial Automation

Background:

Pharmaceutical defect detection in blister packages is challenging, with traditional methods being inefficient and costly.
Automated defect detection using YOLO models is prevalent but requires enhancements for real-time applications.
Existing methods struggle with accuracy and computational overhead in continuous manufacturing environments.

Purpose of the Study:

To propose an enhanced YOLOv8 model, CBS-YOLOv8, for improved real-time defect detection in pharmaceutical manufacturing.
To enhance feature extraction and reduce computational complexity for faster and more accurate defect identification.
To validate the model's performance against existing methods using custom and public datasets.

Main Methods:

The study enhances the YOLOv8 architecture by integrating coordinate attention for improved feature extraction.
Weighted bi-directional feature pyramid network (BiFPN) is incorporated for better feature fusion and reduced information loss.
A simple spatial pyramid pooling fast (SimSPPF) module is implemented to decrease computational demands and increase speed.
A custom dataset of defective pharmaceutical tablets was utilized for model training and evaluation.

Main Results:

CBS-YOLOv8 achieved a mean Average Precision (mAP) of 97.4% and an inference speed of 79.25 FPS on a custom dataset.
The model outperformed other compared models in defect detection accuracy and processing speed.
On the SESOVERA-ST saline bottle fill level monitoring dataset, CBS-YOLOv8 attained a mAP50 of 99.3%.

Conclusions:

CBS-YOLOv8 offers a significant advancement in automated pharmaceutical defect detection.
The model provides an optimized inspection process, enabling prompt defect identification and correction.
This technology bolsters quality assurance practices in manufacturing settings through enhanced accuracy and efficiency.