This paper presents an AI-based real-time alert verification system designed to enhance railway safety by preventing animal-related accidents. Increasing incidents of train collisions with wild animals, particularly elephants, pose a serious challenge in forest and rural areas. Existing systems mainly rely on manual monitoring, which is not effective for real-time detection and response.
To address this issue, the proposed system integrates sensor-based detection with artificial intelligence techniques. A PIR sensor is used for initial motion detection, and an ESP32-CAM captures images for further analysis. The captured images are processed using a Convolutional Neural Network (CNN) model to verify the presence of animals.
Upon confirmation, the system activates multiple alert mechanisms including buzzer, LED indicators, GSM-based message transmission, and motor control to simulate train stopping. The system ensures real-time detection and verification, reducing false alarms and improving reliability. The implemented system demonstrates an effective and cost-efficient solution for enhancing railway safety and preventing animal collisions.
This proposed system achieves improved accuracy and reduced false alarms compared to existing methods.