DFFNet: An IoT-perceptive dual feature fusion network for general real-time semantic segmentation-Reference-Cited by-同舟云学术

DFFNet: An IoT-perceptive dual feature fusion network for general real-time semantic segmentation

Published:2021-07 Issue: Volume:565 Page:326-343
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Tang Xiangyan,Tu Wenxuan,Li Keqiu,Cheng Jieren

Funder

Key Research and Development Project of Hainan Province

Hainan Province Natural Science Foundation

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference47 articles.

1. E. Romera, J.M. lvarez, L.M. Bergasa, R. Arroyo. Erfnet: efficient residual factorized convnet for real-time semantic segmentation, IEEE Trans. Intell. Transp. Syst. 19(1) (2017) 263–272

2. Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening;Li;Inf. Sci.,2019

3. High-resolution encoder-decoder networks for low-contrast medical image segmentation;Zhou;IEEE Trans. Image Process.,2019

4. Fine-grained segmentation using hierarchical dilated neural networks;Zhou,2018

5. U-net: convolutional networks for biomedical image segmentation;Ronneberger,2015

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