Work

Improving the energy efficiency of sorting centers by identifying objects and digit-letter information with neural networks

Year: 2021

Type: conference-paper

Abstract: 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%.

Source: E3S Web of Conferences

DOI: 10.1051/e3sconf/202127901017

Authors: Evgeniy Ivliev, Pavel Obukhov, Viktor Ivliev, Denis Medvedev, Viktor Martynov

Institution: Don State Technical University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

OpenAlex ID: W3200330634