{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T21:54:01Z","timestamp":1780091641518,"version":"3.54.0"},"reference-count":20,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2017,9,29]],"date-time":"2017-09-29T00:00:00Z","timestamp":1506643200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"publisher","award":["610012\/2011-8"],"award-info":[{"award-number":["610012\/2011-8"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The submarine exploration using robots has been increasing in recent years. The automation of tasks such as monitoring, inspection, and underwater maintenance requires the understanding of the robot\u2019s environment. The object recognition in the scene is becoming a critical issue for these systems. On this work, an underwater object classification pipeline applied in acoustic images acquired by Forward-Looking Sonar (FLS) are studied. The object segmentation combines thresholding, connected pixels searching and peak of intensity analyzing techniques. The object descriptor extract intensity and geometric features of the detected objects. A comparison between the Support Vector Machine, K-Nearest Neighbors, and Random Trees classifiers are presented. An open-source tool was developed to annotate and classify the objects and evaluate their classification performance. The proposed method efficiently segments and classifies the structures in the scene using a real dataset acquired by an underwater vehicle in a harbor area. Experimental results demonstrate the robustness and accuracy of the method described in this paper.<\/jats:p>","DOI":"10.3390\/s17102235","type":"journal-article","created":{"date-parts":[[2017,9,29]],"date-time":"2017-09-29T12:24:04Z","timestamp":1506687844000},"page":"2235","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Object Classification in Semi Structured Enviroment Using Forward-Looking Sonar"],"prefix":"10.3390","volume":"17","author":[{"given":"Matheus","family":"Dos Santos","sequence":"first","affiliation":[{"name":"NAUTEC-Intelligent Robotics and Automation Group-Center for Computer Science, Universidade Federal do Rio Grande, Rio Grande 96203-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9237-9745","authenticated-orcid":false,"given":"Pedro","family":"Ribeiro","sequence":"additional","affiliation":[{"name":"NAUTEC-Intelligent Robotics and Automation Group-Center for Computer Science, Universidade Federal do Rio Grande, Rio Grande 96203-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pedro","family":"N\u00fa\u00f1ez","sequence":"additional","affiliation":[{"name":"ROBOLAB - Robotics Laboratory, Department of Computer and Communication Technology, Universidad de Extremadura, C\u00e1ceres, Extremadura 1003, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paulo","family":"Drews-Jr","sequence":"additional","affiliation":[{"name":"NAUTEC-Intelligent Robotics and Automation Group-Center for Computer Science, Universidade Federal do Rio Grande, Rio Grande 96203-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Silvia","family":"Botelho","sequence":"additional","affiliation":[{"name":"NAUTEC-Intelligent Robotics and Automation Group-Center for Computer Science, Universidade Federal do Rio Grande, Rio Grande 96203-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,9,29]]},"reference":[{"key":"ref_1","unstructured":"Thrun, S. 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