Deep Learning Approach at the Edge to Detect Iron Ore Type
1
Graduate Program in Instrumentation, Control and Automation of Mining Processes, Instituto Tecnológico Vale, Federal University of Ouro Preto, Ouro Preto 35400-000, Brazil
2
VALE S.A., Parauapebas, Para 68516-000, Brazil
3
Computing Department, Federal University of Ouro Preto, Ouro Preto 35400-000, Brazil
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Academic Editor: Roberto Teti
Sensors 2022, 22(1), 169; https://doi.org/10.3390/s22010169
Received: 31 October 2021
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Revised: 14 December 2021
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Accepted: 18 December 2021
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Published: 28 December 2021
(This article belongs to the Section Intelligent Sensors)
There 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.
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Keywords:
edge AI; DNN; iron ore quality; AIoT
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MDPI and ACS Style
Klippel, E.; Bianchi, A.G.C.; Delabrida, S.; Silva, M.C.; Garrocho, C.T.B.; Moreira, V.d.S.; Oliveira, R.A.R. Deep Learning Approach at the Edge to Detect Iron Ore Type. Sensors 2022, 22, 169. https://doi.org/10.3390/s22010169
AMA Style
Klippel E, Bianchi AGC, Delabrida S, Silva MC, Garrocho CTB, Moreira VdS, Oliveira RAR. Deep Learning Approach at the Edge to Detect Iron Ore Type. Sensors. 2022; 22(1):169. https://doi.org/10.3390/s22010169
Chicago/Turabian StyleKlippel, Emerson, Andrea G.C. Bianchi, Saul Delabrida, Mateus C. Silva, Charles T.B. Garrocho, Vinicius d.S. Moreira, and Ricardo A.R. Oliveira. 2022. "Deep Learning Approach at the Edge to Detect Iron Ore Type" Sensors 22, no. 1: 169. https://doi.org/10.3390/s22010169
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