Klippel, EmersonBianchi, Andrea Gomes CamposSilva, Saul Emanuel DelabridaSilva, Mateus CoelhoGarrocho, Charles Tim BatistaMoreira, Vinicius da SilvaOliveira, Ricardo Augusto Rabelo2022-10-102022-10-102022KLIPPEL, E. et al. Deep learning approach at the edge to detect iron ore type. Sensors, v. 22, n. 1, artigo 169, 2022. Disponível em: <https://www.mdpi.com/1424-8220/22/1/169?type=check_update&version=1>. Acesso em: 27 set. 2022.1424-8220http://www.repositorio.ufop.br/jspui/handle/123456789/15658There is a constant risk of iron ore collapsing during its transfer between processing stages in beneficiation plants. Existing instrumentation is not only expensive but also complex and challenging to maintain. In this research, we propose using edge artificial intelligence for early detection of landslide risk based on images of iron ore transported on conveyor belts. During this work, we defined the device edge and the deep neural network model. Then, we built a prototype will to collect images that will be used for training the model. This model will be compressed for use in the device edge. This same prototype will be used for field tests of the model under operational conditions. In building the prototype, a real-time clock was used to ensure the synchronization of image records with the plant’s process information, ensuring the correct classification of images by the process specialist. The results obtained in the field tests of the prototype with an accuracy of 91% and a recall of 96% indicate the feasibility of using deep learning at the edge to detect the type of iron ore and prevent its risk of avalanche.en-USabertoEdge AIIron ore qualityArtigo publicado em periodicoThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Fonte: o PDF do artigo.https://doi.org/10.3390/s22010169