This paper provides a structured survey of deep learning techniques for anomaly detection in railway systems. It categorizes existing methods based on anomaly location, data characteristics, and temporal aspects, covering various architectures like CNNs, RNNs, and transformers. The survey also addresses practical deployment challenges such as edge-cloud architectures and computational constraints, offering a framework to guide the selection and implementation of deep learning solutions for intelligent railway monitoring. AI
IMPACT Provides a structured reference for selecting and deploying deep learning solutions for railway anomaly detection.
RANK_REASON The item is a structured survey paper on arXiv detailing deep learning applications in a specific domain. [lever_c_demoted from research: ic=1 ai=1.0]
- anomaly detection
- arXiv
- autoencoder
- convolutional neural network
- deep learning
- generative adversarial network
- Hugging Face
- machine learning
- transformers
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