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Ensemble Simulation Model-Based Animal Intrusion Detection System

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
B. N. Lohith Kumar
N. Manish
N. V. Uma Reddy
S. Sreejith
Published in
Computer Vision and Robotics
Publisher
Springer Nature Switzerland

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Abstract

This chapter explores the development of an ensemble simulation model for detecting animal intrusions on roads, a critical issue contributing to road accidents in India. The model leverages thermal images to identify animals such as dogs, cats, and cattle, as well as potholes, with high accuracy. The study initially trains two deep learning models, MobileNetV2 and NASNetMobile, on thermal images, achieving accuracies of 85.19% and 68.52% respectively. To enhance detection accuracy, a stacking ensemble model is proposed, combining the strengths of both base models. This ensemble model achieves an impressive accuracy of 100%, effectively classifying different types of animals and potholes. The chapter also reviews related works in intrusion detection systems, highlighting the use of advanced algorithms like the coyote optimization algorithm and fuzzy C-means clustering. The conclusion emphasizes the need for such intelligent AI systems to promptly alert drivers, thereby reducing the severity of animal-related road accidents, especially during nighttime.
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previous chapter A Performance Analysis of RC Filter for the Application of Analog Devices
next chapter Large Language Model Interface for Manipulator Control
Title
Ensemble Simulation Model-Based Animal Intrusion Detection System
Authors
B. N. Lohith Kumar
N. Manish
N. V. Uma Reddy
S. Sreejith
Copyright Year
2026
Book
Computer Vision and Robotics

Print ISBN: 978-3-032-06252-9

Electronic ISBN: 978-3-032-06253-6

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-3-032-06253-6_11
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