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A COMPUTER VISION-BASED APPROACH FOR STORAGE LOCATIONS OCCUPANCY DETECTION USING DEEP LEARNING

1 JELEŃ Łukasz
Co-authors:
2 KARKULA Marek 3 OLEARCZUK Dariusz
Institutions:
1 Wroclaw University of Technology, Department of Computer Engineering, Wroclaw, Poland, EU, lukasz.jelen@pwr.edu.pl
2 AGH University of Science and Technology, Faculty of Management, Kraków, Poland, EU, mkarkula@agh.edu.pl
3 Optidata Sp. z o.o., Kraków, Poland, EU, dariusz.olearczuk@optidata.pl
Conference:
10th Carpathial Logistics Congress, Hotel Pod Zámkom, Bojnice, Slovakia, EU, June 15 - 17, 2022
Proceedings:
Proceedings 10th Carpathial Logistics Congress
Pages:
73-79
ISBN:
978-80-88365-08-2
ISSN:
2694-9318
DOI:
https://doi.org/10.37904/clc.2022.4570
Published:
31st October 2022
Proceedings of the conference have been sent to Web of Science and Scopus for evaluation and potential indexing.
Metrics:
353 views / 369 downloads
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Abstract

Increasing the efficiency of processes in warehouse facilities is now required in every industry. One of the important decision-making problems is the proper utilization of storage space. The paper presents research results on the application of architecture for storage location occupancy detection based on computer vision methods and deep learning models. The paper contains a detailed description of the developed solution and an estimation of the solution performance.

Keywords: Occupancy management, storage, computer vision, deep learning, image processing

© This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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