Adaptive Wild Animal Detection System

Abstract

Wildlife encroachment into human settlements is a growing problem across forest-border communities in India and many parts of the world. Animals like elephants and leopards can cause serious harm in seconds, and existing camera-based monitoring systems often miss them or raise alarms too late because their detection models were trained once and never updated. We built an Adaptive Wild Animal Detection System to address this gap. The system uses YOLO for real-time species detection from live CCTV footage and pairs it with an administrator-managed image library, so new animals can be added to the detection pool without rebuilding the model from scratch. When something is detected, the system checks whether that animal is a high-risk or low-risk species and routes the alert accordingly. Six species were covered in our current build, and the system handled real-time video without notable lag. We believe this kind of adaptive, alert-aware design is what practical wildlife monitoring actually needs.

Introduction

The text describes the challenges faced by villages near forests in southern India, where wildlife such as elephants, tigers, and leopards frequently enter human areas, causing damage and danger. Existing monitoring methods, like manual patrols and camera traps, are slow and reactive, often detecting animals only after incidents occur.

Although modern computer vision models like YOLO enable real-time animal detection, most existing systems are limited because they are static—unable to recognize new species without time-consuming retraining. Additionally, collecting labeled data for rare animals is difficult, and current systems lack intelligent alert mechanisms, often treating all detections equally.

To address these issues, the proposed system introduces a real-time wildlife detection and alert platform using YOLO integrated with live CCTV feeds. It allows administrators to add new animal species dynamically through a web interface without modifying the core system. The system also includes a two-tier alert mechanism that distinguishes between high-risk animals (e.g., tigers) and low-risk ones (e.g., deer), ensuring appropriate responses and reducing alert fatigue.

The architecture consists of modules for video input, detection, decision-making, alerting, and an admin dashboard. It runs locally to reduce latency and dependency on internet connectivity. Overall, the system improves adaptability, real-time responsiveness, and practical usability in wildlife monitoring, addressing key gaps in existing solutions.

Conclusion

We set out to build a wildlife detection system that could keep up with what happens in the field. Static models and undifferentiated alerts were not good enough — we needed something that could learn new species without weeks of overhead, and that could tell the difference between a harmless deer and a dangerous predator at the forest boundary. The architecture we settled on — camera ingestion, YOLO inference, event decision, tiered alert dispatch, and Flask admin dashboard — holds up in practice. Each component is independent enough to be upgraded without disrupting the others, which matters for long-running field deployments. The whole system runs locally without cloud dependency, which makes it viable in remote areas. Testing confirmed that the core goals were met real-time detection works, the adaptive enrolment process works, and the alert tiers behave as intended. The gaps we found — low-light accuracy, species confusion in ambiguous conditions — give us a clear roadmap. Infrared camera integration and better preprocessing for nighttime footage are the highest priorities. We also want to test edge deployment on lower-power hardware to bring the installation cost down for communities that need it most.

References

[1] S. Kaplan, M. A. Guvensan, A. G. Yavuz, and Y. Karalurt, \"Driver behaviour analysis for safe driving: A survey,\" IEEE Transactions on Intelligent Transportation Systems, 2015. [2] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, \"You only look once: Unified, real-time object detection,\" IEEE CVPR, 2016. [3] A. Norouz Zadeh et al., \"Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning,\" PNAS, 2018. [4] M. Scheirer, A. Rocha, A. Sapkota, and T. Boult, \"Toward open set recognition,\" IEEE TPAMI, 2013. [5] J. Chappidi and Divya, \"Cascaded YOLOv8 with adaptive preprocessing for nighttime animal detection,\" IEEE, 2024. [6] K. Parkavi et al., \"Detection of animals on highways during night using deep learning,\" IEEE, 2024. [7] C. S. Madhumathi, \"Advanced wild animal detection using YOLOv5 with alert system,\" IEEE, 2023. [8] Nishanth I and Rakeshkumar R, \"Real-time wild animal detection using YOLOv10,\" Elsevier, 2024.

Copyright

Copyright © 2026 Salman S, Sharjik Ahamed S, Vikram B, Nagul Sundar U, Ramachandran R. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.