Railway Track Defects Detection: From a Comprehensive Review of Methods to New Embedded System Modelling Perspectives

The expansion and increasing complexity of railway infrastructure, combined with a growing demand for higher safety and maintenance standards, has driven important innovation in rail defect detection. This review examines recent methods for railway track defect identification, with a particular focus on their deployment on embedded computing architectures. Detection methods are categorized across multiple sensing modalities—vision, acoustics, vibration, and electromagnetic—while highlighting recent advances in deep learning (DL). This study addresses the critical gap between the performance of algorithms and their potential to be deployed on hardware architectures to design reliable, real-time systems. This review evaluates these approaches in terms of suitability for real-time onboard deployment, identifies their limitations, and proposes a new multisensory, embedded system that will balance performance, energy efficiency, and scalability. A study of detection methods and their in-depth evaluation aims to bridge the gap between complex and high-accuracy detection algorithms and their integration into lighter railway monitoring systems.