Open Access

ARTICLE

Pyramid–MixNet: Integrate Attention into Encoder-Decoder Transformer Framework for Automatic Railway Surface Damage Segmentation

Hui Luo, Wenqing Li^*, Wei Zeng

School of Information and Software Engineering, East China Jiaotong University, Nanchang, 330013, China

* Corresponding Author: Wenqing Li. Email:

(This article belongs to the Special Issue: Artificial Intelligence and Advanced Computation Technology in Railways)

Computers, Materials & Continua 2025, 84(1), 1567-1580. https://doi.org/10.32604/cmc.2025.062949

Received 31 December 2024; Accepted 18 April 2025; Issue published 09 June 2025

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.

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