| Issue |
E3S Web Conf.
Volume 279, 2021
III International Conference “Energy Efficiency and Energy Saving in Technical Systems” (EEESTS-2021)
|
|
|---|---|---|
| Article Number | 01017 | |
| Number of page(s) | 6 | |
| Section | Modern Energy Efficient Automation Technology | |
| DOI | https://doi.org/10.1051/e3sconf/202127901017 | |
| Published online | 01 July 2021 | |
Improving the energy efficiency of sorting centers by identifying objects and digit-letter information with neural networks
Don State Technical University, Gagarin square 1, 344016 Rostov-on-Don, Russia
* Corresponding author: 123ivliev123@mail.ru
The article is devoted to the development and analysis of methods of identifying dynamic objects. A neural network with the architecture of SSD MobileNetV2 has been developed to solve the problem of detecting baggage tags and barcodes. Several approaches are considered to solve the problem of identifying digital-letter information: Tesseract, SSD InceptionV2, OpenCV and a convolutional neural network. The efficiency of the methods on real images was checked. It was concluded that electricity consumption can be reduced by 49.43%.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.