Detection of ship targets in photoelectric images based on an improved recurrent attention convolutional neural network
Deep learning algorithms have been increasingly used in ship image detection and classification. To improve the ship detection and classification in photoelectric images, an improved recurrent attention convolutional neural network is proposed. The proposed network has a multi-scale architecture and...
Main Authors: | Zhijing Xu, Yuhao Huo, Kun Liu, Sidong Liu |
---|---|
Format: | Article |
Language: | English |
Published: |
SAGE Publishing
2020-03-01
|
Series: | International Journal of Distributed Sensor Networks |
Online Access: | https://doi.org/10.1177/1550147720912959 |
Similar Items
-
Broad Area Target Search System for Ship Detection via Deep Convolutional Neural Network
by: Yanan You, et al.
Published: (2019-08-01) -
Efficient Attention Mechanism for Dynamic Convolution in Lightweight Neural Network
by: Enjie Ding, et al.
Published: (2021-03-01) -
Double attention recurrent convolution neural network for answer selection
by: Ganchao Bao, et al.
Published: (2020-05-01) -
A Cascade Coupled Convolutional Neural Network Guided Visual Attention Method for Ship Detection From SAR Images
by: Juanping Zhao, et al.
Published: (2018-01-01) -
NSD-SSD: A Novel Real-Time Ship Detector Based on Convolutional Neural Network in Surveillance Video
by: Jiuwu Sun, et al.
Published: (2021-01-01)