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Multimodal Deep Learning Based Wildlife Intrusion Perception Using YOLOv12 and YAMNet

Author

Listed:
  • Vamshi Krishna Velpula

    (Student, Department of Computer Science and Engineering, Rajiv Gandhi University of Knowledge Technologies, Basar)

  • Arun Kumar Ankeshwarapu

    (Student, Department of Computer Science and Engineering, Rajiv Gandhi University of Knowledge Technologies, Basar)

  • Madhu Kumar Bolle

    (Student, Department of Computer Science and Engineering, Rajiv Gandhi University of Knowledge Technologies, Basar)

  • Dr. B. Venkat Raman

    (Assistant Professor, HOD, Department of Computer Science and Engineering, Rajiv Gandhi University of Knowledge Technologies, Basar)

Abstract

Crop damage caused by wildlife intrusion is a major challenge for farmers near forest boundaries. Traditional monitoring methods are labor-intensive and ineffective under poor visibility conditions. This paper proposes a multi-modal wildlife intrusion detection system that combines visual object detection and environmental sound classification. The system utilizes the YOLOv12 model for real-time animal detection from surveillance video and YAMNet for identifying animal sounds. By integrating visual and auditory sensing, the proposed framework improves detection reliability in low-light or occluded conditions. Experimental evaluation demonstrates improved detection accuracy compared to single-modal approaches. The system can be deployed on edge devices such as Raspberry Pi or Jetson Nano, enabling real-time monitoring of agricultural fields.

Suggested Citation

  • Vamshi Krishna Velpula & Arun Kumar Ankeshwarapu & Madhu Kumar Bolle & Dr. B. Venkat Raman, 2026. "Multimodal Deep Learning Based Wildlife Intrusion Perception Using YOLOv12 and YAMNet," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 13(4), pages 436-445, April.
    • Handle: RePEc:bjc:journl:v:13:y:2026:i:4:p:436-445
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