PT Journal
AU Klos, M
   Pagani, P
TI Using Deep Neural Networks to Measure Puffer Levels in Real-time with Edge-Computing
SO Logistics Journal : Proceedings
PY 2021
VL 2021
IS 17
DI 10.2195/lj_Proc_klos_en_202112_01
DE Computer Vision; Deep Learning; Einplatinencomputer; Object Detection; Objekterkennung; Single-Board Computer; YOLO
AB Technological advances and increasing data traffic in the IoT environment lead to the relocation of sophisticated data processing to the edge of networks. At the same time, powerful object detection approaches based on deep neural networks have been developed in recent years. In this paper, an intelligent camera based on deep learning algorithms and consisting of low-cost hardware with limited computational and storage capacity is presented. The developed object detection solution enables real-time monitoring of the inventory of filled and empty small load carriers in a buffer zone.
ER