Rail Detection

Rail detection, crucial for automated train operation and safety, focuses on accurately identifying railway tracks in various conditions using computer vision and sensor data. Current research emphasizes improving the speed and accuracy of detection, particularly for small-scale defects and in challenging environments, employing deep learning architectures like convolutional neural networks (CNNs), Swin Transformers, and recurrent neural networks (NARX) alongside innovative approaches such as dynamic anchor lines and row-based detection. These advancements are driving progress towards more efficient and reliable railway inspection systems, autonomous train operation, and improved safety measures.