Object Detection and Train Disaster Avoidance on Railway Track Using AI-Based Wireless Sensor Network

Abstract

The system integrates Artificial Intelligence (AI) with embedded hardware to detect obstacles or objects present on the railway track and respond in real time to prevent accidents. An AI-based object detection model, developed using Python and implemented in PyCharm, processes live video or image data from a connected camera or PC. When the model identifies an object on the track, it sends a detection signal to the ESP32 microcontroller through serial communication or Wi-Fi. Upon receiving this signal, the ESP32 immediately turns off the relay, stopping the DC motor that represents the train movement. Simultaneously, a 16×2 LCD display connected to the ESP32 shows the system status such as “Object Detected – Train Stopped.” Current railway safety mechanisms often rely on manual inspection or fixed track sensors, which are insufficient for preventing accidents caused by unpredictable foreign objects, debris, or unauthorized presence on the track in real time. Additionally, the system can be scaled for real-world deployment by integrating high-resolution cameras and cloud-based data analysis for continuous monitoring. Future enhancements may include automatic alert notifications to railway authorities through GSM or IoT platforms. Overall, this approach contributes to safer, smarter, and more reliable railway transportation systems.