Detection and Classification of Subsea Objects in Forward-Looking Sonar and Electro-Optical Sensors for ROV Autonomy

被引:1
|
作者
Nguyen, Vincente [1 ]
Bezanson, Leverett [1 ]
Kinnamman, Ben [2 ]
机构
[1] SeeByte Inc, Edinburgh, Midlothian, Scotland
[2] Greensea Syst Inc, Richmond, VA USA
来源
2022 OCEANS HAMPTON ROADS | 2022年
关键词
D O I
10.1109/OCEANS47191.2022.9977122
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
SeeByte and Greensea Systems, Inc. (Greensea) have teamed up with the sponsorship of the Defense Innovation Unit (DIU) and PMS 408 to advance Automatic Target Recognition (ATR) of Mine Like Objects (MLOs) for the purpose of developing an autonomous capability for Remotely Operated Vehicles (ROVs) used in maritime Explosive Ordnance Disposal operations. Advancements in autonomy requires advancements in the sensor processing to detect the surrounding environment and identify objects of interest. In this case the sensors chosen were a dual frequency Forward Looking Sonar and a Electro-Optical (EO) stereo camera system. The objects of interest are MLOs that need to be located, identified, and inspected by an autonomous submersible robot. SeeByte used Deep Learning Neural Network (DNN) on both of these sensor feeds yielding a very robust detection and classification system. The output of that detection and classification is provided to Greensea's vehicle control and autonomy software as candidate targets for mapping and prosecution.
引用
收藏
页数:8
相关论文
共 50 条
  • [21] Research on Obstacle Detection and Avoidance of Autonomous Underwater Vehicle Based on Forward-Looking Sonar
    Cao, Xiang
    Ren, Lu
    Sun, Changyin
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, 34 (11) : 9198 - 9208
  • [22] Frequency agile detection performance for a fast-moving forward-looking active SONAR
    Mirkin, A. N.
    Giorgianni, J. P.
    OCEANS 2005, VOLS 1-3, 2005, : 2038 - 2043
  • [23] Objectness Scoring and Detection Proposals in Forward-Looking Sonar Images with Convolutional Neural Networks
    Valdenegro-Toro, Matias
    ARTIFICIAL NEURAL NETWORKS IN PATTERN RECOGNITION, 2016, 9896 : 209 - 219
  • [24] A Shadow Capture Deep Neural Network for Underwater Forward-Looking Sonar Image Detection
    Xiao, Taowen
    Cai, Zijian
    Lin, Cong
    Chen, Qiong
    MOBILE INFORMATION SYSTEMS, 2021, 2021
  • [25] Target Detection of Forward-Looking Sonar Image Based on Improved YOLOv5
    Zhang, Haoting
    Tian, Mei
    Shao, Gaoping
    Cheng, Juan
    Liu, Jingjing
    IEEE ACCESS, 2022, 10 : 18023 - 18034
  • [26] Tracking Drifting Surface Objects with Aerial Infrared and Electro-Optical Sensors
    Krout, David W.
    Okopal, Greg
    Jessup, Andy
    Hanusa, Evan
    2012 OCEANS, 2012,
  • [27] The convolution neural network based agent vehicle detection using forward-looking sonar image
    Kim, Juhwan
    Cho, Hyeonwoo
    Pyo, Juhyun
    Kim, Byeongjin
    Yu, Son-Cheol
    OCEANS 2016 MTS/IEEE MONTEREY, 2016,
  • [28] UUVDNet: An efficient unmanned underwater vehicle target detection network for multibeam forward-looking sonar
    Zhang, Xuyang
    Pan, Han
    Jing, Zhongliang
    Ling, Kaiyao
    Peng, Pai
    Song, Buer
    OCEAN ENGINEERING, 2025, 315
  • [29] RFLNet: Reverse Feature Learning Network for Salient Object Detection in Forward-Looking Sonar Images
    He, Fu-Lin
    Wang, Zhen
    Yuan, Shen-Ao
    Zhang, Shan-Wen
    Zhao, Zheng-Yang
    IEEE ACCESS, 2024, 12 : 155437 - 155450
  • [30] Underwater Forward-Looking Sonar Images Target Detection via Speckle Reduction and Scene Prior
    Long, Hui
    Shen, Liquan
    Wang, Zhengyong
    Chen, Jinbo
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2023, 61