Deep Learning Framework for Real-Time Animal Detection in Rural Surveillance Applications

The growing frequency of human wildlife conflicts in rural and agricultural areas necessitates advanced monitoring systems capable of detecting animal intrusions in real time. Traditional manual methods and static sensor networks often fail to provide timely alerts, especially under low-light conditions or partial occlusions. This study proposes a deep learning-based animal detection framework designed to enhance real-time surveillance using Convolutional Neural Networks (CNN) integrated with transfer learning techniques. The system leverages pre-trained models such as YOLOv5, Faster R-CNN, and SSD, fine tuned with domain specific datasets including COCO and ImageNet, to classify and localize multiple animal species across diverse environments. A virtual deterrent mechanism, simulating loud cracker sounds, is incorporated to non-lethally repel detected animals and minimize crop damage or livestock threats. Experimental evaluations on rural video feeds and thermal imagery demonstrate an average detection accuracy of 92% in daylight and 86% in low-light scenarios, with a frame processing speed of 22 FPS on an edge device. The aim of this research is to develop a scalable, low-latency, and cost effective animal detection system capable of real-time response, particularly suited for remote agricultural surveillance. The proposed framework significantly improves detection precision and operational speed compared to conventional methods, enabling proactive interventions in human wildlife conflict zones. Future enhancements include behavior based analysis, multi-modal sensor integration, and adaptive alert systems for improved specificity and resilience. Despite promising results, challenges remain in detecting small or fast-moving animals in cluttered environments, presenting a key gap for future exploration.