AI-based acoustic wave monitoring of rail defects like cracks, fractures and prediction for rail wear, and anomalies.
The IoT project is focused on removing the risks of train derailing with the help of,
Main module - to identify frequency issues &
Child module - to visual data and processes the information to assess defect severity.
Alerts are displayed on UIs for train supervisors and station admins, enabling quick action.
The system features two distinct UIs, each serving different purposes. Users can monitor their location, communicate with nearby stations, and call for emergencies when needed. Additionally, instructions can be relayed through these interfaces to enhance coordination.
Child module, located at the base of the Unity Poles, includes an ESP-CAM capable of object detection. It captures images, summarizes the condition, and sends the image metadata to the Main Module for further processing.
Main module fixated on the bottom of the train consists of Raspberry Pi, which will perform all the AI related tasks using TinyML, and will provide output of all the inputs received.