{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T11:48:18Z","timestamp":1770896898382,"version":"3.50.1"},"reference-count":67,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,12,14]],"date-time":"2022-12-14T00:00:00Z","timestamp":1670976000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Federal Center for Technological Education Celso Suckow da Fonseca","award":["001"],"award-info":[{"award-number":["001"]}]},{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfeicoamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"publisher","award":["001"],"award-info":[{"award-number":["001"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"National Council for Scientific and Technological Development","doi-asserted-by":"publisher","award":["001"],"award-info":[{"award-number":["001"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004586","name":"Funda\u00e7\u00e3o Carlos Chagas Filho de Amparo \u00e0 Pesquisa do Estado do Rio de Janeiro","doi-asserted-by":"publisher","award":["001"],"award-info":[{"award-number":["001"]}],"id":[{"id":"10.13039\/501100004586","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Research Centre in Digitalization and Intelligent Robotics (CeDRI)","award":["001"],"award-info":[{"award-number":["001"]}]},{"DOI":"10.13039\/501100015322","name":"Polytechnic Institute of Bragan\u00e7a","doi-asserted-by":"publisher","award":["001"],"award-info":[{"award-number":["001"]}],"id":[{"id":"10.13039\/501100015322","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Laborat\u00f3rio para a Sustentabilidade e Tecnologia em Regi\u00f5es de Montanha (SusTEC)","award":["001"],"award-info":[{"award-number":["001"]}]},{"name":"INESC Technology and Science","award":["001"],"award-info":[{"award-number":["001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Drones"],"abstract":"<jats:p>Unmanned Aerial Systems (UAS) are becoming more attractive in diverse applications due to their efficiency in performing tasks with a reduced time execution, covering a larger area, and lowering human risks at harmful tasks. In the context of Oil &amp; Gas (O&amp;G), the scenario is even more attractive for the application of UAS for inspection activities due to the large extension of these facilities and the operational risks involved in the processes. Many authors proposed solutions to detect gas leaks regarding the onshore unburied pipeline structures. However, only a few addressed the navigation and tracking problem for the autonomous navigation of UAS over these structures. Most proposed solutions rely on traditional computer vision strategies for tracking. As a drawback, depending on lighting conditions, the obtained path line may be inaccurate, making a strategy to force the UAS to continue on the path necessary. Therefore, this research describes the potential of an autonomous UAS based on image processing technique and Convolutional Neural Network (CNN) strategy to navigate appropriately in complex unburied pipeline networks contributing to the monitoring procedure of the Oil &amp; Gas Industry structures. A CNN is used to detect the pipe, while image processing techniques such as Canny edge detection and Hough Transform are used to detect the pipe line reference, which is used by a line following algorithm to guide the UAS along the pipe. The framework is assessed by a PX4 flight controller Software-in-The-Loop (SITL) simulations performed with the Robot Operating System (ROS) along with the Gazebo platform to simulate the proposed operational environment and verify the approach\u2019s functionality as a proof of concept. Real tests were also conducted. The results showed that the solution is robust and feasible to deploy in this proposed task, achieving 72% of mean average precision on detecting different types of pipes and 0.0111 m of mean squared error on the path following with a drone 2 m away from a tube.<\/jats:p>","DOI":"10.3390\/drones6120410","type":"journal-article","created":{"date-parts":[[2022,12,14]],"date-time":"2022-12-14T02:54:21Z","timestamp":1670986461000},"page":"410","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Computer Vision Based Path Following for Autonomous Unmanned Aerial Systems in Unburied Pipeline Onshore Inspection"],"prefix":"10.3390","volume":"6","author":[{"given":"Yago","family":"da Silva","sequence":"first","affiliation":[{"name":"Department of Electronics Engineering, Federal Center for Technological Education of Rio de Janeiro, Rio de Janeiro 20271-110, Brazil"}]},{"given":"Fabio","family":"Andrade","sequence":"additional","affiliation":[{"name":"Department of Microsystems, Faculty of Technology, Natural Sciences and Maritime Sciences, University of South-Eastern Norway (USN), 3184 Borre, Norway"},{"name":"NORCE\u2014Norwegian Research Centre, 5838 Bergen, Norway"}]},{"given":"Lucas","family":"Sousa","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Federal Center for Technological Education of Rio de Janeiro, Rio de Janeiro 20271-110, Brazil"}]},{"given":"Gabriel","family":"de Castro","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Federal Center for Technological Education of Rio de Janeiro, Rio de Janeiro 20271-110, Brazil"}]},{"given":"Jo\u00e3o","family":"Dias","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Federal Center for Technological Education of Rio de Janeiro, Rio de Janeiro 20271-110, Brazil"}]},{"given":"Guido","family":"Berger","sequence":"additional","affiliation":[{"name":"Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Polit\u00e9cnico de Bragan\u00e7a, 5300-252 Bragan\u00e7a, Portugal"},{"name":"Laborat\u00f3rio para a Sustentabilidade e Tecnologia em Regi\u00f5es de Montanha (SusTEC), Instituto Polit\u00e9cnico de Bragan\u00e7a, 5300-252 Bragan\u00e7a, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7902-1207","authenticated-orcid":false,"given":"Jos\u00e9","family":"Lima","sequence":"additional","affiliation":[{"name":"Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Polit\u00e9cnico de Bragan\u00e7a, 5300-252 Bragan\u00e7a, Portugal"},{"name":"Laborat\u00f3rio para a Sustentabilidade e Tecnologia em Regi\u00f5es de Montanha (SusTEC), Instituto Polit\u00e9cnico de Bragan\u00e7a, 5300-252 Bragan\u00e7a, Portugal"},{"name":"INESC TEC\u2014Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6916-700X","authenticated-orcid":false,"given":"Milena","family":"Pinto","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Federal Center for Technological Education of Rio de Janeiro, Rio de Janeiro 20271-110, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Pinto, M.F., Melo, A.G., Marcato, A.L., and Urdiales, C. 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