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Sensors 2017, 17(10), 2235; doi:10.3390/s17102235

Object Classification in Semi Structured Enviroment Using Forward-Looking Sonar

1
NAUTEC-Intelligent Robotics and Automation Group-Center for Computer Science, Universidade Federal do Rio Grande, Rio Grande 96203-900, Brazil
2
ROBOLAB - Robotics Laboratory, Department of Computer and Communication Technology, Universidad de Extremadura, Cáceres, Extremadura 1003, Spain
*
Author to whom correspondence should be addressed.
Received: 16 July 2017 / Revised: 29 August 2017 / Accepted: 12 September 2017 / Published: 29 September 2017
(This article belongs to the Special Issue Advances and Challenges in Underwater Sensor Networks)
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Abstract

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’s 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. View Full-Text
Keywords: underwater sensors; underwater monitoring; underwater surveillance underwater sensors; underwater monitoring; underwater surveillance
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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dos Santos, M.; Ribeiro, P.O.; Núñez, P.; Drews-Jr, P.; Botelho, S. Object Classification in Semi Structured Enviroment Using Forward-Looking Sonar. Sensors 2017, 17, 2235.

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