An improved DeepLabv3 + railway track extraction algorithm based on densely connected and attention mechanisms
Abstract The railway track extraction using unmanned aerial vehicle (UAV) aerial images suffers from issues such as low extraction accuracy and high time consumption. In response to these problems, this paper presents a lightweight algorithm DA-DeepLabv3 + based on densely connected and attention me...
Saved in:
Main Authors: | Yanbin Weng, Jie Yang, Changfan Zhang, Jing He, Cheng Peng, Lin Jia, Hui Xiang |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2025-01-01
|
Series: | Scientific Reports |
Subjects: | |
Online Access: | https://doi.org/10.1038/s41598-024-84937-5 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
TransDeep: Transformer-Integrated DeepLabV3+ for Image Semantic Segmentation
by: Tengfei Chai, et al.
Published: (2025-01-01) -
RailPC: A large‐scale railway point cloud semantic segmentation dataset
by: Tengping Jiang, et al.
Published: (2024-12-01) -
A study of attention and imagery capacities in badminton players
by: Gulsum Bastug, et al.
Published: (2017-08-01) -
Enhancing Road Scene Segmentation With an Optimized DeepLabV3+
by: Zhe Ren, et al.
Published: (2024-01-01) -
Multiscale guided attention network for optic disc segmentation of retinal images
by: A Z M Ehtesham Chowdhury, et al.
Published: (2025-01-01)