{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T17:42:04Z","timestamp":1771954924644,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,11,27]],"date-time":"2022-11-27T00:00:00Z","timestamp":1669507200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"ERDF\u2014European Regional Development Fund","award":["POCI-01-0247-FEDER-048183"],"award-info":[{"award-number":["POCI-01-0247-FEDER-048183"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotics"],"abstract":"<jats:p>Object identification, such as tree trunk detection, is fundamental for forest robotics. Intelligent vision systems are of paramount importance in order to improve robotic perception, thus enhancing the autonomy of forest robots. To that purpose, this paper presents three contributions: an open dataset of 5325 annotated forest images; a tree trunk detection Edge AI benchmark between 13 deep learning models evaluated on four edge-devices (CPU, TPU, GPU and VPU); and a tree trunk mapping experiment using an OAK-D as a sensing device. The results showed that YOLOR was the most reliable trunk detector, achieving a maximum F1 score around 90% while maintaining high scores for different confidence levels; in terms of inference time, YOLOv4 Tiny was the fastest model, attaining 1.93 ms on the GPU. YOLOv7 Tiny presented the best trade-off between detection accuracy and speed, with average inference times under 4 ms on the GPU considering different input resolutions and at the same time achieving an F1 score similar to YOLOR. This work will enable the development of advanced artificial vision systems for robotics in forestry monitoring operations.<\/jats:p>","DOI":"10.3390\/robotics11060136","type":"journal-article","created":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T01:58:46Z","timestamp":1669600726000},"page":"136","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Edge AI-Based Tree Trunk Detection for Forestry Monitoring Robotics"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9999-1550","authenticated-orcid":false,"given":"Daniel Queir\u00f3s","family":"da Silva","sequence":"first","affiliation":[{"name":"INESC Technology and Science (INESC TEC), 4200-465 Porto, Portugal"},{"name":"School of Science and Technology, University of Tr\u00e1s-os-Montes e Alto Douro (UTAD), 5000-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8486-6113","authenticated-orcid":false,"given":"Filipe Neves","family":"dos Santos","sequence":"additional","affiliation":[{"name":"INESC Technology and Science (INESC TEC), 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3747-6577","authenticated-orcid":false,"given":"V\u00edtor","family":"Filipe","sequence":"additional","affiliation":[{"name":"INESC Technology and Science (INESC TEC), 4200-465 Porto, Portugal"},{"name":"School of Science and Technology, University of Tr\u00e1s-os-Montes e Alto Douro (UTAD), 5000-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0317-4714","authenticated-orcid":false,"given":"Armando Jorge","family":"Sousa","sequence":"additional","affiliation":[{"name":"INESC Technology and Science (INESC TEC), 4200-465 Porto, Portugal"},{"name":"Faculty of Engineering, University of Porto (FEUP), 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4283-1243","authenticated-orcid":false,"given":"Paulo Moura","family":"Oliveira","sequence":"additional","affiliation":[{"name":"INESC Technology and Science (INESC TEC), 4200-465 Porto, Portugal"},{"name":"School of Science and Technology, University of Tr\u00e1s-os-Montes e Alto Douro (UTAD), 5000-801 Vila Real, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1038\/s41586-020-2438-y","article-title":"Abrupt increase in harvested forest area over Europe after 2015","volume":"583","author":"Ceccherini","year":"2020","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"118986","DOI":"10.1016\/j.foreco.2021.118986","article-title":"Application of conventional UAV-based high-throughput object detection to the early diagnosis of pine wilt disease by deep learning","volume":"486","author":"Wu","year":"2021","journal-title":"For. 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