![Yash01](https://circuitdigest.com/contest/iot-edge-ai-project-challenge-2024/wp-content/uploads/ultimatemember/700/cover_photo-300.jpg?1739344929)
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This project aims to develop an autonomous drone-based system for real-time detection and monitoring of train lines using deep learning and IoT technologies. The system will utilize a drone equipped with a high-resolution camera, onboard AI processing, and communication modules to identify and track train lines from aerial footage. The system will be capable of detecting potential obstacles or damages along the tracks, providing real-time alerts to maintenance teams for prompt action. Deep Learning-Based Detection: Train line detection is achieved using a deep learning model (e.g., YOLO, Faster R-CNN) trained to recognize and segment train tracks from aerial images captured by the drone.
Edge AI Processing: The trained model is deployed on the drone’s onboard AI device (e.g., Sipeed Maixduino), enabling real-time inference and decision-making during flight without relying on cloud processing. The drone is equipped with communication modules (e.g., Wi-Fi, 4G/5G) to transmit detected track conditions, coordinates, and alerts to a central server or mobile app for remote monitoring. The system uses GPS data to geotag detected tracks, creating a real-time map of the train line's condition, which can be accessed by maintenance teams for targeted interventions. The system generates immediate alerts if it detects anomalies such as track damage, obstacles, or deviations from the standard track pattern, helping to prevent accidents or delays. By continuously monitoring and logging data over time, the system supports predictive maintenance strategies, identifying potential issues before they become critical.