Density-Aware U-Net for Unstructured Environment Dust Segmentation-Reference-Cited by-同舟云学术

Density-Aware U-Net for Unstructured Environment Dust Segmentation

Published:2024-03-15 Issue:6 Volume:24 Page:8210-8226
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Fu Yao¹^ORCID,Gao Ming¹^ORCID,Xie Guotao¹^ORCID,Hu Manjiang¹^ORCID,Wei Chenfeng¹^ORCID,Ding Rongjun¹^ORCID

Affiliation:

1. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China

Funder

National Key Research and Development Program of China

NSF of China

Science and Technology Innovation Program of Hunan Province

Changsha NSF of China

State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7361/10472410/10418077.pdf?arnumber=10418077

Reference77 articles.

1. An Efficient Deep Convolutional Neural Network for Semantic Segmentation

2. REF-Net: Robust, Efficient, and Fast Network for Semantic Segmentation Applications Using Devices With Limited Computational Resources

3. Improved U-Net: Fully Convolutional Network Model for Skin-Lesion Segmentation

4. FU-Net: fast biomedical image segmentation model based on bottleneck convolution layers

5. AEDCN-Net: Accurate and Efficient Deep Convolutional Neural Network Model for Medical Image Segmentation

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