AI-Driven Deep Learning-Based Animal Intrusion Detection and Early Warning System for Railroad Tracks

Abstract

This paper presents an AI-driven animal intrusion detection and warning system designed to improve safety on railway tracks. The proposed framework applies deep learning and computer vision techniques, particularly Convolutional Neural Networks (CNN) and EfficientNet, to automatically analyze captured images and identify animals entering railway track regions. These AI models perform feature extraction, detection, and classification of animals based on learned visual characteristics. Once an intrusion is identified, the system generates an immediate warning to the locomotive driver and railway control center through a GSM module and buzzer. Detected animal information, together with the corresponding date and time, is stored in a cloud-based database for centralized monitoring and further analysis. The system focuses on intelligent detection and timely warning, while train-speed control remains under the manual supervision of the loco pilot. The collected intrusion data can further support AI-assisted analysis of animal movement patterns and facilitate the development of preventive railway safety strategies.