{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T12:31:12Z","timestamp":1770726672437,"version":"3.49.0"},"reference-count":32,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,28]],"date-time":"2021-12-28T00:00:00Z","timestamp":1640649600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3390\/s22010169","type":"journal-article","created":{"date-parts":[[2021,12,28]],"date-time":"2021-12-28T01:20:43Z","timestamp":1640654443000},"page":"169","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Deep Learning Approach at the Edge to Detect Iron Ore Type"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0312-3615","authenticated-orcid":false,"given":"Emerson","family":"Klippel","sequence":"first","affiliation":[{"name":"Graduate Program in Instrumentation, Control and Automation of Mining Processes, Instituto Tecnol\u00f3gico Vale, Federal University of Ouro Preto, Ouro Preto 35400-000, Brazil"},{"name":"VALE S.A., Parauapebas, Para 68516-000, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7949-1188","authenticated-orcid":false,"given":"Andrea Gomes Campos","family":"Bianchi","sequence":"additional","affiliation":[{"name":"Computing Department, Federal University of Ouro Preto, Ouro Preto 35400-000, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8961-5313","authenticated-orcid":false,"given":"Saul","family":"Delabrida","sequence":"additional","affiliation":[{"name":"Computing Department, Federal University of Ouro Preto, Ouro Preto 35400-000, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3717-1906","authenticated-orcid":false,"given":"Mateus Coelho","family":"Silva","sequence":"additional","affiliation":[{"name":"Computing Department, Federal University of Ouro Preto, Ouro Preto 35400-000, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8245-306X","authenticated-orcid":false,"given":"Charles Tim Batista","family":"Garrocho","sequence":"additional","affiliation":[{"name":"Computing Department, Federal University of Ouro Preto, Ouro Preto 35400-000, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1803-0131","authenticated-orcid":false,"given":"Vinicius da Silva","family":"Moreira","sequence":"additional","affiliation":[{"name":"VALE S.A., Parauapebas, Para 68516-000, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5167-1523","authenticated-orcid":false,"given":"Ricardo Augusto Rabelo","family":"Oliveira","sequence":"additional","affiliation":[{"name":"Computing Department, Federal University of Ouro Preto, Ouro Preto 35400-000, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1016\/j.jclepro.2018.06.106","article-title":"A comparative outline for quantifying risk ratings in occupational health and safety risk assessment","volume":"196","author":"Gul","year":"2018","journal-title":"J. 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