@article{Luo2025, 
author = {Hui Luo and Wenqing Li and Wei Zeng},
title = {Pyramid–MixNet: Integrate Attention into Encoder-Decoder Transformer Framework for Automatic Railway Surface Damage Segmentation},
year = {2025},
journal = {Computers, Materials & Continua},
volume = {84},
number = {1},
pages = {1567-1580},
keywords = {Pyramid vision transformer, encoder–decoder architecture, railway damage segmentation, masked multi-head attention, mix-attention},
url = {https://www.sciopen.com/article/10.32604/cmc.2025.062949},
doi = {10.32604/cmc.2025.062949},
abstract = {Rail surface damage is a critical component of high-speed railway infrastructure, directly affecting train operational stability and safety. Existing methods face limitations in accuracy and speed for small-sample, multi-category, and multi-scale target segmentation tasks. To address these challenges, this paper proposes Pyramid-MixNet, an intelligent segmentation model for high-speed rail surface damage, leveraging dataset construction and expansion alongside a feature pyramid-based encoder-decoder network with multi-attention mechanisms. The encoding network integrates Spatial Reduction Masked Multi-Head Attention (SRMMHA) to enhance global feature extraction while reducing trainable parameters. The decoding network incorporates Mix-Attention (MA), enabling multi-scale structural understanding and cross-scale token group correlation learning. Experimental results demonstrate that the proposed method achieves 62.17% average segmentation accuracy, 80.28% Damage Dice Coefficient, and 56.83 FPS, meeting real-time detection requirements. The model’s high accuracy and scene adaptability significantly improve the detection of small-scale and complex multi-scale rail damage, offering practical value for real-time monitoring in high-speed railway maintenance systems.}
}