AI-Based Animal Detection and Alert System: A Real-Time CNN-Powered Wildlife Surveillance and Notification Framework

Abstract

Wildlife detection and monitoring play a critical role in preventing human–animal conflicts and supporting biodiversity conservation. Traditional monitoring methods relying on camera traps produce large volumes of image data that require extensive manual analysis, making them inefficient and error-prone. Deep Learning, particularly Convolutional Neural Networks (CNNs), offers a powerful solution by automating animal recognition with high accuracy and speed. This paper presents the design and implementation of an AI-based wild animal detection and alert system that leverages CNN-based image classification integrated with OpenCV for real-time video processing. The proposed system detects and classifies wild animals from live camera feeds and automatically triggers alerts to enable rapid response. The system architecture incorporates six modules: camera and image acquisition, preprocessing, AI-based animal detection, alert generation, database management, and a user interface dashboard. Experimental results demonstrate high classification accuracy and real-time performance, making the system suitable for deployment in forest-adjacent areas, wildlife reserves, and rural zones. Future work includes edge computing deployment and multi-animal simultaneous detection.