@Article{cmc.2025.062949,
AUTHOR = {Hui Luo, Wenqing Li, Wei Zeng},
TITLE = {Pyramid–MixNet: Integrate Attention into Encoder-Decoder Transformer Framework for Automatic Railway Surface Damage Segmentation},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {84},
YEAR = {2025},
NUMBER = {1},
PAGES = {1567--1580},
URL = {http://www.techscience.com/cmc/v84n1/61723},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2025.062949}
}