Open Access

ARTICLE

MSCM-Net: Rail Surface Defect Detection Based on a Multi-Scale Cross-Modal Network

Xin Wen^*, Xiao Zheng, Yu He

School of Software Engineering, Shenyang University of Technology, Shenyang, 110870, China

* Corresponding Author: Xin Wen. Email:

(This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition, 2nd Edition)

Computers, Materials & Continua 2025, 82(3), 4371-4388. https://doi.org/10.32604/cmc.2025.060661

Received 07 November 2024; Accepted 21 January 2025; Issue published 06 March 2025

Abstract

Detecting surface defects on unused rails is crucial for evaluating rail quality and durability to ensure the safety of rail transportation. However, existing detection methods often struggle with challenges such as complex defect morphology, texture similarity, and fuzzy edges, leading to poor accuracy and missed detections. In order to resolve these problems, we propose MSCM-Net (Multi-Scale Cross-Modal Network), a multiscale cross-modal framework focused on detecting rail surface defects. MSCM-Net introduces an attention mechanism to dynamically weight the fusion of RGB and depth maps, effectively capturing and enhancing features at different scales for each modality. To further enrich feature representation and improve edge detection in blurred areas, we propose a multi-scale void fusion module that integrates multi-scale feature information. To improve cross-modal feature fusion, we develop a cross-enhanced fusion module that transfers fused features between layers to incorporate interlayer information. We also introduce a multimodal feature integration module, which merges modality-specific features from separate decoders into a shared decoder, enhancing detection by leveraging richer complementary information. Finally, we validate MSCM-Net on the NEU RSDDS-AUG RGB-depth dataset, comparing it against 12 leading methods, and the results show that MSCM-Net achieves superior performance on all metrics.

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Keywords

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