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An improved DeepLabv3 + railway track extraction algorithm based on densely connected and attention mechanisms
Yanbin Weng1, Jie Yang2, Changfan Zhang3
1School of Computer Science, Hunan University of Technology, Tianyuan District, Zhuzhou, 412007, China. wengyb@hut.edu.cn.
Scientific Reports
|January 20, 2025
Summary
This study introduces DA-DeepLabv3+, a lightweight algorithm for railway track extraction from aerial images. It improves accuracy and reduces processing time compared to existing methods.
Area of Science:
- Computer Vision
- Machine Learning
- Remote Sensing
Background:
- Railway track extraction from Unmanned Aerial Vehicle (UAV) imagery faces challenges with accuracy and efficiency.
- Existing deep learning models often have high computational costs and parameter counts.
Purpose of the Study:
- To develop a lightweight and accurate algorithm for railway track segmentation using UAV aerial images.
- To address the limitations of low extraction accuracy and high time consumption in current methods.
Main Methods:
- Proposed DA-DeepLabv3+ algorithm utilizing MobileNetV2 for reduced parameters.
- Incorporated atrous spatial pyramid pooling (ASPP) with cascading atrous convolutions and a multi-scale attention module.
- Developed a multi-level upsampling module for enhanced boundary contour extraction.
Main Results:
- Achieved 87.52% mIoU and 97.59% accuracy on a dedicated railway track dataset.
- Attained 85.01% mIoU and 94.84% accuracy on the DeepGlobe dataset.
- Demonstrated superior performance over U-Net and DeepLabv3+ in accuracy and speed.
Conclusions:
- DA-DeepLabv3+ offers a significant advancement in automated railway track extraction.
- The algorithm provides a computationally efficient and highly accurate solution for UAV-based railway monitoring.
