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Pierleoni P, Belli A, Palma L, Sabbatini L. A Versatile Machine Vision Algorithm for Real-Time Counting Manually Assembled Pieces. Journal of Imaging. 2020; 6(6):48. https://doi.org/10.3390/jimaging6060048

Pierleoni, P.; Belli, A.; Palma, L.; Sabbatini, L. A Versatile Machine Vision Algorithm for Real-Time Counting Manually Assembled Pieces. J. Imaging 2020, 6, 48. https://doi.org/10.3390/jimaging6060048

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Pierleoni, Paola, Alberto Belli, Lorenzo Palma, and Luisiana Sabbatini. 2020. "A Versatile Machine Vision Algorithm for Real-Time Counting Manually Assembled Pieces" Journal of Imaging 6, no. 6: 48. https://doi.org/10.3390/jimaging6060048

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Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Abstract: The Industry 4.0 paradigm is based on transparency and co-operation and, hence, on monitoring and pervasive data collection. In highly standardized contexts, it is usually easy to gather data using available technologies, while, in complex environments, only very advanced and customizable technologies, such as Computer Vision, are intelligent enough to perform such monitoring tasks well. By the term “complex environment”, we especially refer to those contexts where human activity which cannot be fully standardized prevails. In this work, we present a Machine Vision algorithm which is able to effectively deal with human interactions inside a framed area. By exploiting inter-frame analysis, image pre-processing, binarization, morphological operations, and blob detection, our solution is able to count the pieces assembled by an operator using a real-time video input. The solution is compared with a more advanced Machine Learning-based custom object detector, which is taken as reference. The proposed solution demonstrates a very good performance in terms of Sensitivity, Specificity, and Accuracy when tested on a real situation in an Italian manufacturing firm. The value of our solution, compared with the reference object detector, is that it requires no training and is therefore extremely flexible, requiring only minor changes to the working parameters to translate to other objects, making it appropriate for plant-wide implementation. Keywords: Industry 4.0; machine vision; machine Learning; aggregated channel features detector; blob detection; smart workstation

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Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

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