Semantic Segmentation of River Video for Efficient River Surveillance System

Author:

Inoue Haruki1,Katayama Takafumi2,Song Tian2,Shimamoto Takashi2

Affiliation:

1. Tokushima University,Department of Science and Engineering,Tokushima City,Japan,770-8506

2. Institute of Technology and Science, Graduate School of Engineering Tokushima University,Tokushima City,Japan,770-8506

Publisher

IEEE

Reference8 articles.

1. Deep Learning-Based Maritime Environment Segmentation for Unmanned Surface Vehicles Using Superpixel Algorithms

2. Semantic object classes in video: A high-definition ground truth database

3. U-Net: convolutional networks for biomedical image segmentation;ronneberger;Medical Image Computing and Computer Assisted Intervention,2015

4. SegNet: A deep convolutional encoder-decoder architecture for image segmentation;badrinarayanan;ArXiv,2016

5. Hypernetworks;ha;Google Brain,2016

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Low Bit Rate Video Coding using VVC and DCVC-DC for River Surveillance;2024 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC);2024-07-02

2. Partial Image Generation with VQGAN for River Surveillance Video Coding;2024 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC);2024-07-02

3. Classification of River Sediment Fractions in a River Segment including Shallow Water Areas Based on Aerial Images from Unmanned Aerial Vehicles with Convolution Neural Networks;Remote Sensing;2023-12-31

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