Abstract Railways form a vital part of global transportation networks, supporting economic development and sustainability through the efficient movement of freight and passengers. Maintaining the safety and reliability of these systems requires consistent inspection and monitoring of both infrastructure and rolling stock. Traditional inspection practices, while established, remain labour-intensive, time-consuming, and prone to human error, which can undermine operational reliability. Computer vision has become a key technology for automating these processes by enabling accurate visual assessment of tracks, components, and operational environments. Developments in deep learning have significantly strengthened these capabilities by improving defect detection accuracy, enhancing robustness under varied environmental conditions, and supporting real-time operation. More recently, AI-driven inspection frameworks that incorporate multi-sensor fusion, edge computing, and transformer-based architectures have pushed railway monitoring towards predictive, scalable, and increasingly autonomous maintenance solutions. This review provides a comprehensive and structured synthesis of research spanning track inspection, rolling stock monitoring, and passenger and operational safety. It evaluates recent methodological advances, summarises the strengths and limitations of current approaches, and identifies ongoing challenges related to data availability, computational constraints, and deployment in dynamic outdoor settings. The evidence indicates that track inspection technologies are closest to large-scale practical adoption, while rolling stock and safety monitoring systems are developing rapidly but still require further refinement for widespread deployment. By offering an integrated assessment of the current technological landscape and outlining opportunities for future research, this review supports researchers, industry practitioners, and policymakers in progressing towards safer, more efficient, and more sustainable railway systems enabled by modern computer vision and AI techniques.
Alif et al. (Thu,) studied this question.