Next Article in Journal
Finite Element Analysis of Grouting Compactness Monitoring in a Post-Tensioning Tendon Duct Using Piezoceramic Transducers
Next Article in Special Issue
Optimal Deployment of Sensor Nodes Based on Performance Surface of Underwater Acoustic Communication
Previous Article in Journal
A Machine Learning Approach to Argo Data Analysis in a Thermocline
Previous Article in Special Issue
EDOVE: Energy and Depth Variance-Based Opportunistic Void Avoidance Scheme for Underwater Acoustic Sensor Networks
Open AccessArticle

Object Classification in Semi Structured Enviroment Using Forward-Looking Sonar

NAUTEC-Intelligent Robotics and Automation Group-Center for Computer Science, Universidade Federal do Rio Grande, Rio Grande 96203-900, Brazil
ROBOLAB - Robotics Laboratory, Department of Computer and Communication Technology, Universidad de Extremadura, Cáceres, Extremadura 1003, Spain
Author to whom correspondence should be addressed.
Sensors 2017, 17(10), 2235;
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)
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
Show Figures

Figure 1

MDPI and ACS Style

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop