ECNet: Efficient Convolutional Networks for Side Scan Sonar Image Segmentation

ECNet: Efficient Convolutional Networks for Side Scan Sonar Image Segmentation

This paper presents a novel and practical convolutional neural network architecture to implement semantic segmentation for side scan sonar (SSS) image. As a widely used sensor for marine survey, SSS provides higher-resolution images of the seafloor and underwater target. However, for a large number...

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Bibliographic Details
Main Authors: Meihan Wu, Qi Wang, Eric Rigall, Kaige Li, Wenbo Zhu, Bo He, Tianhong Yan
Format: Article
Language:English
Published: MDPI AG 2019-04-01
Series:Sensors
Subjects:
side scan sonar (SSS)
semantic segmentation
imbalance classification
image-to-image prediction
fully convolutional neural networks
deeply-supervised nets
Online Access:https://www.mdpi.com/1424-8220/19/9/2009

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