International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Deep Learning Based Estimation of Wild Animal Activity Detection Using Hybrid Neural Network |
|---|---|
| ABSTRACT | The increasing frequency of animal attacks poses a significant concern for rural communities and forestry personnel. To monitor the movements of wild animals, tools such as surveillance cameras and drones are commonly used. However, an effective system is needed that can identify the species, track its movements, and provide location details. This information can then be used to send timely alerts to safeguard people and forestry workers. Although computer vision and machine learning techniques are widely applied for animal detection, they are often costly and complex, limiting their practical effectiveness. In response, a novel real-time wildlife monitoring system has been developed using YOLOv12, which is capable of functioning accurately in challenging environments, with varying animal postures and under low-light conditions. Alerts generated by this system are automatically sent to the local forest office via email for immediate action. The proposed approach demonstrates significant improvements in performance, achieving an average classification accuracy of 98%. The system was evaluated both qualitatively and quantitatively on a dataset of 40,000 images across three benchmark datasets containing 25 animal classes, yielding mean accuracy and precision above 98%. Overall, this model provides a reliable solution for delivering precise animal-related information and ensuring the safety of humans. |
| AUTHOR | RAJARAJESWARI T, G.KOWSALYA, K.VIJAYPRABAKARAN, K.SARVESHWARI, VINOPRIYA K Dept. of Computer Science and Engineering, Gnyanamani College of Technology, Namakkal, India Assistant Professor, Dept. of Computer Science and Engineering, Gnyanamani College of Technology, Namakkal, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1406099 |
| pdf/99_Deep Learning Based Estimation of Wild Animal Activity Detection Using Hybrid Neural Network.pdf | |
| KEYWORDS | |
| References | 1. B. Natarajan, R. Elakkiya, R. Bhuvaneswari, K. Saleem, D. Chaudhary & S. H. Samsudeen, “Creating Alert Messages Based on Wild Animal Activity Detection Using Hybrid Deep Neural Networks,” IEEE Access, vol. 11, pp. 67308–67321, 2023. 2. J. Kline, R. Katole, T. Berger Wolf & C. Stewart, “Edge Native, Behavior Adaptive Drone System for Wildlife Monitoring,” arXiv preprint, 2025. 3. X. Li, “Wild Animals Detection Based on YOLOv5,” Applied and Computational Engineering, vol. 8, pp. 612–622, 2023. 4. S. Li, H. Zhang & F. Xu, “Intelligent Detection Method for Wildlife Based on Deep Learning,” Sensors, vol. 23, no. 24, 9669, 2023. 5. Authors Unknown, “Wildlife Real Time Detection in Complex Forest Scenes Based on YOLOv5s Deep Learning Network,” Remote Sensing, vol. 16, no. 8, 1350, 2024. 6. Ponuganti J. Chandra Rao & P. Rohini Bai, “A Hybrid Deep Learning Framework for Wild Animal Detection and Automated Alert Messaging,” International Journal of Engineering Research and Science & Technology, vol. 21, no. 2, pp. 1006–1015, 2025. 7. Authors Unknown, “Wild Animal Movement Detection and Alert System,” International Journal of Scientific Research in CSE & IT, 2025. 8. L. Saad Saoud, L. Lesobre, E. Sorato & I. Hussain, “Real Time Threaded Houbara Detection and Segmentation for Wildlife Conservation Using Mobile Platforms,” arXiv preprint, 2025. 9. Authors Unknown, “Wild Animal Detection Using CNN,” International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023. 10. Shahrir A. Suttan, Z. Ayop, S. Anawar & N. M. Zainudin, “A Hybrid YOLOv5 SSD IoT Based Animal Detection System for Durian Plantation Protection,” arXiv preprint, 2025. 11. Authors Unknown, “WildARe YOLO: A Lightweight and Efficient Wild Animal Recognition Model,” Ecological Informatics, vol. 80, 102541, 2024. 12. Authors Unknown, “Overview and Comparison of Deep Neural Networks for Wildlife Recognition Using Infrared Images,” MDPI, vol. 5, no. 4, 135, 2023. 13. Authors Unknown, “A Review of Deep Learning Techniques for Detecting Animals in Aerial and Satellite Images,” Journal of Applied Geodesy, 2024. 14. N. Jain, N. Gupta, A. K. Agarwal, M. Sharma & S. Datta, “A Deep Learning Based Model for the Detection and Tracking of Animals for Safety and Management of Financial Loss,” Journal of Electrical Systems, vol. 20, no. 2s, 2024. 15. Y. A. Roopashree, M. Bhoomika, R. Priyanka, K. Nisarga & S. Behera, “Monitoring the Movements of Wild Animals and Alert System using Deep Learning Algorithm,” In Proceedings of RTEICT 2021, Bengaluru, 2021. 16. H. Nguyen, S. J. Maclagan, T. D. Nguyen, T. Nguyen, P. Flemons, K. Andrews, E. G. Ritchie & D. Phung, “Animal Recognition and Identification with Deep Convolutional Neural Networks for Automated Wildlife Monitoring,” In Proceedings of IEEE DSAA 2017, Tokyo, 2017. 17. P. Ji & Q. Zhu, “Research on Embedded Animal Recognition System Based on YOLO,” In Proceedings of ICRAS 2022, Wuhan, China, 2022. 18. Various Authors, “Machine Learning Based Wildlife Trap Camera System with CNN Classification,” International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2024. 19. Authors Unknown, “Wildlife Target Detection Based on Improved YOLOX s Network,” Scientific Reports, 2024. 20. Andres Hernandez, Z. Miao, L. Vargas, S. Beery, R. Dodhia, P. Arbelaez & J. M. Lavista Ferres, “Pytorch Wildlife: A Collaborative Deep Learning Framework for Conservation,” arXiv preprint, 2024. |
