A lightweight multi-scale deformable CNN-transformer dual-branch network for detecting rail sleeper cracks

Rail sleepers are critical to the safe operation of trains but are susceptible to cracking due to prolonged load pressure and weather-related corrosion. These cracks pose serious challenges to operational safety. However, existing crack detection algorithms often struggle to accurately identify sleeper cracks in complex real-world environments. To address this issue, we propose LM2DNet, which combines the local detail feature extraction capabilities of CNNs with the global semantic context captured by transformers. Using a lightweight, multi-scale deformable CNN-Transformer two-branch fusion architecture, LM2DNet integrates both feature types effectively. This fusion enables accurate identification of fine-grained crack details while preserving semantic information about the sleepers, ultimately improving detection accuracy and recall while reducing computational overhead. LM2DNet achieves a 2.2% performance boost over other CNN-Transformer fusion networks and outperforms existing sleeper crack detection methods by 3.0%. Deployed in over 30 high-speed rail and urban subway systems, it has successfully detected more than 6000 sleeper cracks, proving its effectiveness in complex and challenging environments.