CNN Based Wildlife Intrusion Detection and Alert System

Authors

  • G. Ravi Department of Electronics and Communication Engineering, Andhra Loyola institute of Engineering and Technology, Vijayawada-520008, India  
  • Sd. Afroz Department of Electronics and Communication Engineering, Andhra Loyola institute of Engineering and Technology, Vijayawada-520008, India  
  • K. Yamuna Department of Electronics and Communication Engineering, Andhra Loyola institute of Engineering and Technology, Vijayawada-520008, India  
  • Sd. Afsha Department of Electronics and Communication Engineering, Andhra Loyola institute of Engineering and Technology, Vijayawada -520008, India

Keywords:

CNN, Deep learning, Image processing, Live capturing

Abstract

Wildlife intrusion detection and alert systems are designed to detect and alert wildlife intrusion events, such as animals crossing highways, entering farms or protected areas, and approaching human settlements. This system often uses advanced technologies, such as cameras, sensors, and machine learning algorithms, to detect and identify animal species and behaviors. Convolutional neural networks (CNNs) are a type of deep learning algorithm commonly used for image and video analysis tasks, including object detection and classification. CNNs can learn to extract features from images and videos automatically and accurately, making them well-suited for detecting animals in wildlife intrusion detection systems.

References

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Additional Files

Published

2023-03-31

How to Cite

Ravi, G., Afroz, S., Yamuna, K., & Afsha, S. (2023). CNN Based Wildlife Intrusion Detection and Alert System. International Transactions on Electrical Engineering and Computer Science, 2(1), 30-36. Retrieved from https://iteecs.com/index.php/iteecs/article/view/40

Issue

Vol. 2 No. 1 (2023): March

Section

Articles

License

Copyright (c) 2023 G. Ravi, Sd. Afroz, K. Yamuna, Sd. Afsha

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.