Abstract
It is obvious that tablet image tracking exerts a notable influence on the efficiency and reliability of high-speed drug mass production, and, simultaneously, it also emerges as a big difficult problem and targeted focus during production monitoring in recent years, due to the high similarity shape and random position distribution of those objectives to be searched for. For the purpose of tracking tablets accurately in random distribution, through using surface fitting approach and transitional vector determination, the calibrated surface of light intensity reflective energy can be established, describing the shape topology and topography details of objective tablet. On this basis, the mathematical properties of these established surfaces have been proposed, and thereafter artificial neural network (ANN) has been employed for classifying those moving targeted tablets by recognizing their different surface properties; therefore, the instantaneous coordinate positions of those drug tablets on one image frame can then be determined. By repeating identical pattern recognition on the next image frame, the real-time movements of objective tablet templates were successfully tracked in sequence. This paper provides reliable references and new research ideas for the real-time objective tracking in the case of drug production practices.
Highlights
The automation level of drug mass production and quality inspection still remains undeveloped; most drug tablets in random position distribution were manually inspected for picking out those unqualified ones and hand-encapsulated after amount counting or bottle packaging by ocular estimation
Literature [27,28,29] presented their latest progresses in cross-correlation mechanisms while such evaluation approaches as intuitionistic probability, intervalvalued probability sets, and gamma rank correlation coefficient were, respectively, described in [30,31,32]. Since these commonly used approaches were found being influenced by traditional limitations, the real-time tablet tracking cannot be ensured ; more importantly, due to the fact that most existing image tracking technologies focus on human tracking or vehicle monitoring, which can be distinguished by the different shapes and the widely divergent sizes of those targeted objects in practice [33,34,35], the high similarity objective tracking method suitable for high-speed drug mass production still remains unstudied and undeveloped, that becomes a research margin of advanced video inspection in these years
The weight values in artificial neural network (ANN) network markedly affect the classification of tablet templates, which keep a close correlation with the computation or tracking errors in the proposed experimental conditions; they were impacted by the arrangement of neural neutrons and input/output vectors as well as weight values provide correlation among network layers, which ensures the feasibility of recognizing the geometrical shapes of tablet template and the property distribution of its reflective energy surface with the selfadaptive control of ANN
Summary
The automation level of drug mass production and quality inspection still remains undeveloped; most drug tablets in random position distribution were manually inspected for picking out those unqualified ones and hand-encapsulated after amount counting or bottle packaging by ocular estimation. Literature [27,28,29] presented their latest progresses in cross-correlation mechanisms while such evaluation approaches as intuitionistic probability, intervalvalued probability sets, and gamma rank correlation coefficient were, respectively, described in [30,31,32] Since these commonly used approaches were found being influenced by traditional limitations, the real-time tablet tracking cannot be ensured ; more importantly, due to the fact that most existing image tracking technologies focus on human tracking or vehicle monitoring, which can be distinguished by the different shapes and the widely divergent sizes of those targeted objects in practice [33,34,35], the high similarity objective tracking method suitable for high-speed drug mass production still remains unstudied and undeveloped, that becomes a research margin of advanced video inspection in these years.
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