Automated Machine Vision System for Liquid Particle Inspection of Pharmaceutical Injection

The particle matter inspection for pharmaceutical injection is inevitable in the field of pharmaceutical manufacturing, as it has the direct impact on the quality of the drugs. It is a challenge to inspect the contaminated injection online using an imaging system. This paper introduces a novel and e...

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Veröffentlicht in:IEEE transactions on instrumentation and measurement 2018-06, Vol.67 (6), p.1278-1297
Hauptverfasser: Zhang, Hui, Li, Xuanlun, Zhong, Hang, Yang, Yimin, Wu, Q. M. Jonathan, Ge, Ji, Wang, Yaonan
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Sprache:eng
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Zusammenfassung:The particle matter inspection for pharmaceutical injection is inevitable in the field of pharmaceutical manufacturing, as it has the direct impact on the quality of the drugs. It is a challenge to inspect the contaminated injection online using an imaging system. This paper introduces a novel and effective inspection machine consisting of three modules, a mechanical system with 120 carousel grips, an image acquisition system with multihigh resolution cameras and a multilight sources station, and a distributed industrial electrical computer control system. Particle visual inspection machine first acquires image sequence using the high-speed image acquisition system. The image capture process at each camera module is alternately synchronized with different LED illumination techniques (light transmission method and light reflection method), enabling independent capture of particle images from the same container. Then, a set of novel algorithms for image registration and fast segmentation are proposed to minimize false rejections even in sensitive conditions, which enable the identification of all the tiny potential defects. Finally, a particle tracking and classification algorithm based on an adaptive local weighted-collaborative sparse model is also presented. The experiments demonstrate that the proposed inspection system can effectively detect the particles in the pharmaceutical infusion solution online, and achieve a performance rate of above 97% average accuracy.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2018.2800258