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Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics
Mobile robotics in forests is currently a hugely important topic due to the recurring appearance of forest wildfires. Thus, in-site management of forest inventory and biomass is required. To tackle this issue, this work presents a study on detection at the ground level of forest tree trunks in visib...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8468268/ https://www.ncbi.nlm.nih.gov/pubmed/34564102 http://dx.doi.org/10.3390/jimaging7090176 |
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author | da Silva, Daniel Queirós dos Santos, Filipe Neves Sousa, Armando Jorge Filipe, Vítor |
author_facet | da Silva, Daniel Queirós dos Santos, Filipe Neves Sousa, Armando Jorge Filipe, Vítor |
author_sort | da Silva, Daniel Queirós |
collection | PubMed |
description | Mobile robotics in forests is currently a hugely important topic due to the recurring appearance of forest wildfires. Thus, in-site management of forest inventory and biomass is required. To tackle this issue, this work presents a study on detection at the ground level of forest tree trunks in visible and thermal images using deep learning-based object detection methods. For this purpose, a forestry dataset composed of 2895 images was built and made publicly available. Using this dataset, five models were trained and benchmarked to detect the tree trunks. The selected models were SSD MobileNetV2, SSD Inception-v2, SSD ResNet50, SSDLite MobileDet and YOLOv4 Tiny. Promising results were obtained; for instance, YOLOv4 Tiny was the best model that achieved the highest AP (90%) and F1 score (89%). The inference time was also evaluated, for these models, on CPU and GPU. The results showed that YOLOv4 Tiny was the fastest detector running on GPU (8 ms). This work will enhance the development of vision perception systems for smarter forestry robots. |
format | Online Article Text |
id | pubmed-8468268 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84682682021-10-28 Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics da Silva, Daniel Queirós dos Santos, Filipe Neves Sousa, Armando Jorge Filipe, Vítor J Imaging Article Mobile robotics in forests is currently a hugely important topic due to the recurring appearance of forest wildfires. Thus, in-site management of forest inventory and biomass is required. To tackle this issue, this work presents a study on detection at the ground level of forest tree trunks in visible and thermal images using deep learning-based object detection methods. For this purpose, a forestry dataset composed of 2895 images was built and made publicly available. Using this dataset, five models were trained and benchmarked to detect the tree trunks. The selected models were SSD MobileNetV2, SSD Inception-v2, SSD ResNet50, SSDLite MobileDet and YOLOv4 Tiny. Promising results were obtained; for instance, YOLOv4 Tiny was the best model that achieved the highest AP (90%) and F1 score (89%). The inference time was also evaluated, for these models, on CPU and GPU. The results showed that YOLOv4 Tiny was the fastest detector running on GPU (8 ms). This work will enhance the development of vision perception systems for smarter forestry robots. MDPI 2021-09-03 /pmc/articles/PMC8468268/ /pubmed/34564102 http://dx.doi.org/10.3390/jimaging7090176 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This 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/). |
spellingShingle | Article da Silva, Daniel Queirós dos Santos, Filipe Neves Sousa, Armando Jorge Filipe, Vítor Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics |
title | Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics |
title_full | Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics |
title_fullStr | Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics |
title_full_unstemmed | Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics |
title_short | Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics |
title_sort | visible and thermal image-based trunk detection with deep learning for forestry mobile robotics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8468268/ https://www.ncbi.nlm.nih.gov/pubmed/34564102 http://dx.doi.org/10.3390/jimaging7090176 |
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