Abstract: Railway track crack detection systems play a pivotal role in safeguarding the integrity and reliability of railway infrastructure, ensuring the safety of passengers and freight transportation. This comprehensive review paper provides an in-depth analysis of recent advancements in crack detection technologies, focusing on the integration of sensor networks, Arduino microcontrollers, GPS modules, and Internet of Things (IoT) technology. The paper examines the evolution of crack detection methodologies and highlights the challenges associated with ensuring the effectiveness and applicability of these systems across diverse railway environments and conditions. Key challenges addressed include the continuous evolution of detection technologies, the need for generalizability across varied datasets and environmental factors, real-time deployment considerations, data management complexities, interoperability requirements, regulatory compliance obligations, and sustainable maintenance protocols. Furthermore, the paper discusses future research directions, emphasizing the potential for advancements in deep learning algorithms, sensor technologies, and maintenance practices to further optimize crack detection systems. By addressing these challenges and leveraging emerging technologies, railway track crack detection systems can be enhanced to meet the evolving demands of modern railway transportation networks, ensuring their continued safety, reliability, and efficiency.

Keywords: Railway Track Crack Detection, Landslide Detection, Sensor Networks, IOT, Data Management, Deep Learning Algorithms.

| DOI: 10.17148/IARJSET.2024.11458