RT Journal Article
T1 Bridge Crane Monitoring using a 3D LiDAR and Deep Learning.
A1 García, Jesús M.
A1 Martínez-Rodríguez, Jorge Luis
A1 Reina-Terol, Antonio Jesús
K1 Grúas - Control automático
K1 Inteligencia artificial
K1 Redes neuronales (Informática)
K1 Dispositivos de seguridad
AB The use of overhead cranes in warehouses andfactories has advantages for handling and transporting bulkyand/or heavy loads. But it also involves risks such as collisions withother fixed or mobile elements in the working environment.Different types of sensors have been used for monitoring itsoperation, mainly artificial vision. In this paper, it is employed athree-dimensional (3D) LiDAR to capture the workspace of a bridgecrane. The point clouds generated by this laser sensor are deliveredto a convolutional neural network to detect the position of the bridgeand its carriage, which allows to locate the hook and the suspendedload afterwards. Additionally, the laser scans can also be used towarn the operator of possible collisions with fixed elements of thewarehouse. The tests carried out show that the proposed system canbe successfully used for monitoring overhead cranes.
PB IEEE
YR 2023
FD 2023-01-11
LK https://hdl.handle.net/10630/29601
UL https://hdl.handle.net/10630/29601
LA eng
NO J. M. García, J. L. Martínez and A. J. Reina, "Bridge Crane Monitoring using a 3D LiDAR and Deep Learning," in IEEE Latin America Transactions, vol. 21, no. 2, pp. 207-216, Feb. 2023, doi: 10.1109/TLA.2023.10015213
NO POLÍTICA DE ACCESO ABIERTO TOMADA DE: https://v2.sherpa.ac.uk/romeo/search.html
NO Fundación Carolina de España
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 6 mar 2026