Monitoring and Controlling Rail Inspection Train

This project focuses on developing an automated inspection train that uses computer vision and machine learning to monitor railway infrastructure in real time. Equipped with high-resolution cameras and sensors, the system captures images and videos of tracks and surrounding structures, detecting issues like misalignment, wear, and damage through image processing and deep learning. Key technologies include Python, OpenCV, and TensorFlow, aiming to improve railway safety and efficiency by automating inspections, reducing manual labor, and enabling proactive maintenance planning.