Train Track Crack Classification

The primary objective of this project is to create an automated train track crack classification system that accurately distinguishes between Defective and Non-Defective railway tracks. By leveraging deep learning algorithms, including CNN, MobileNet, and ResNet, the system aims to improve the efficiency and accuracy of railway track inspection processes. The system will allow railway operators to upload images of tracks through a web-based interface, where they will receive real-time feedback on whether the track is defective or not. This automated approach will reduce the reliance on manual inspections, which are time-consuming, expensive, and prone to human error. The project’s goal is to provide a solution that can be easily integrated into existing railway maintenance operations, helping identify cracks and defects early, thereby minimizing the risk of accidents and delays caused by track failures. Ultimately, the project aims to enhance the overall safety and reliability of rail transport systems.