TY - GEN
T1 - A Convolutional Neural Network Based on Double-tower Structure for Underwater Terrain Classification
AU - Liu, Wei
AU - Cui, Rongxin
AU - Li, Yang
AU - Liu, Shuqiang
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/11
Y1 - 2019/1/11
N2 - Terrain classification plays a critical role in all robot systems especially in unknown environments. In recent years, researchers have proposed various algorithms to improve the efficiency and accuracy of terrain classification. Nevertheless, these methods still have some deficiencies in classification efficiency. In this paper, a double-tower convolutional neural network has been designed to implement end-to-end underwater terrain classification. The matched sonar image and visual image constitute an image pair, which is obtained at the same time by the sonar sensor and the visual sensor of the robot or underwater vehicle. The corresponding image pairs are set to be the input of the convolutional neural network, and the output of the network is the classification of the terrain. Then, terrain features from sonar and visual images are simultaneously applied to achieve terrain classification. Therefore, an end-to-end convolutional neural network with a classification function has been established in this paper.
AB - Terrain classification plays a critical role in all robot systems especially in unknown environments. In recent years, researchers have proposed various algorithms to improve the efficiency and accuracy of terrain classification. Nevertheless, these methods still have some deficiencies in classification efficiency. In this paper, a double-tower convolutional neural network has been designed to implement end-to-end underwater terrain classification. The matched sonar image and visual image constitute an image pair, which is obtained at the same time by the sonar sensor and the visual sensor of the robot or underwater vehicle. The corresponding image pairs are set to be the input of the convolutional neural network, and the output of the network is the classification of the terrain. Then, terrain features from sonar and visual images are simultaneously applied to achieve terrain classification. Therefore, an end-to-end convolutional neural network with a classification function has been established in this paper.
KW - convolutional neural network
KW - sonar image
KW - terrain classification
KW - visual image
UR - http://www.scopus.com/inward/record.url?scp=85061477956&partnerID=8YFLogxK
U2 - 10.1109/ICARM.2018.8610675
DO - 10.1109/ICARM.2018.8610675
M3 - 会议稿件
AN - SCOPUS:85061477956
T3 - ICARM 2018 - 2018 3rd International Conference on Advanced Robotics and Mechatronics
SP - 697
EP - 702
BT - ICARM 2018 - 2018 3rd International Conference on Advanced Robotics and Mechatronics
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2018
Y2 - 18 July 2018 through 20 July 2018
ER -