{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T01:30:48Z","timestamp":1775007048308,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,10,17]],"date-time":"2021-10-17T00:00:00Z","timestamp":1634428800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"COMPETE 2020","award":["POCI-01-0247-FEDER- 038342"],"award-info":[{"award-number":["POCI-01-0247-FEDER- 038342"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Fire"],"abstract":"<jats:p>Forest fires are still a large concern in several countries due to the social, environmental and economic damages caused. This paper aims to show the design and validation of a proposed system for the classification of smoke columns with object detection and a deep learning-based approach. This approach is able to detect smoke columns visible below or above the horizon. During the dataset labelling, the smoke object was divided into three different classes, depending on its distance to the horizon, a cloud object was also added, along with images without annotations. A comparison between the use of RetinaNet and Faster R-CNN was also performed. Using an independent test set, an F1-score around 80%, a G-mean around 80% and a detection rate around 90% were achieved by the two best models: both were trained with the dataset labelled with three different smoke classes and with augmentation; Faster R-CNNN was the model architecture, re-trained during the same iterations but following different learning rate schedules. Finally, these models were tested in 24 smoke sequences of the public HPWREN dataset, with 6.3 min as the average time elapsed from the start of the fire compared to the first detection of a smoke column.<\/jats:p>","DOI":"10.3390\/fire4040075","type":"journal-article","created":{"date-parts":[[2021,10,18]],"date-time":"2021-10-18T13:59:52Z","timestamp":1634565592000},"page":"75","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":88,"title":["A Deep Learning Based Object Identification System for Forest Fire Detection"],"prefix":"10.3390","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2762-0333","authenticated-orcid":false,"given":"Federico","family":"Guede-Fern\u00e1ndez","sequence":"first","affiliation":[{"name":"Physics Department, NOVA School of Science and Technology (Campus de Caparica), 2829-516 Caparica, Portugal"},{"name":"Future Compta SA, 11495-190 Alges, Portugal"},{"name":"LIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics), NOVA School of Science and Technology (Campus de Caparica), 2829-516 Caparica, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3351-2910","authenticated-orcid":false,"given":"Leonardo","family":"Martins","sequence":"additional","affiliation":[{"name":"Physics Department, NOVA School of Science and Technology (Campus de Caparica), 2829-516 Caparica, Portugal"},{"name":"Future Compta SA, 11495-190 Alges, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2269-7094","authenticated-orcid":false,"given":"Rui Valente","family":"de Almeida","sequence":"additional","affiliation":[{"name":"Physics Department, NOVA School of Science and Technology (Campus de Caparica), 2829-516 Caparica, Portugal"},{"name":"Future Compta SA, 11495-190 Alges, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4022-7424","authenticated-orcid":false,"given":"Hugo","family":"Gamboa","sequence":"additional","affiliation":[{"name":"Physics Department, NOVA School of Science and Technology (Campus de Caparica), 2829-516 Caparica, Portugal"},{"name":"LIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics), NOVA School of Science and Technology (Campus de Caparica), 2829-516 Caparica, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3823-1184","authenticated-orcid":false,"given":"Pedro","family":"Vieira","sequence":"additional","affiliation":[{"name":"Physics Department, NOVA School of Science and Technology (Campus de Caparica), 2829-516 Caparica, Portugal"},{"name":"Future Compta SA, 11495-190 Alges, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.rse.2018.08.005","article-title":"The Collection 6 MODIS burned area mapping algorithm and product","volume":"217","author":"Giglio","year":"2018","journal-title":"Remote Sens. 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