Carnivorous Animal Detection Using AI & ML

Abstract

Carnivorous Animal Detection Project explores the application of deep Learning models, Specifically ResNet-50, for detection and classification of carnivorous animals in diverse ecological for the detection and classification of carnivorous animals in diverse ecological environments. Utilizing advanced computer vision techniques, the system automates the identification of species and their dietary habits, offering an efficient, scalable solution for wildlife monitoring and conservation. By leveraging large datasets of animal images, the model is trained to accurately distinguish between carnivorous and non-carnivorous species, enhancing the understanding of predator-prey dynamics. The system\'s real-time detection capability provides immediate insights, supporting conservation efforts, ecological research, and mitigating human-wildlife conflicts. Future improvements aim to expand the model\'s scope to include more species, refine detection accuracy, and integrate additional data modalities such as acoustic and thermal imaging. This project highlights the potential of AI-driven solutions to advance wildlife conservation, offering a robust tool for researchers and environmental organizations.

Introduction

A. Background

Human expansion and resource exploitation are severely impacting wildlife, altering ecosystems and leading to species extinction or displacement. Observing wildlife in their natural habitats is vital for ecological research and conservation. Technologies like GPS, radio tracking, and motion-sensitive camera traps have improved monitoring. Among these, camera traps are increasingly popular due to their affordability, ease of use, and ability to capture large volumes of images.

B. Objective

While camera traps are efficient at collecting data, managing the massive number of images (many without animals or in poor quality) is labor-intensive and time-consuming. Projects like Snapshot Serengeti and Wildlife Spotter highlight the need for automated image analysis using artificial intelligence to streamline wildlife detection and classification.

II. Literature Review

Recent research leverages deep learning for wildlife image analysis:

• Transfer Learning: Reuses pre-trained models from general datasets for wildlife detection.

• TPRPN Model: A novel architecture called Two-Channel Perceiving Residual Pyramid Networks was introduced for enhanced detection in low-quality images, capturing depth and detail more effectively.

• Performance: New datasets and architectures like TPRPN outperform conventional object detection models.

III. Methodology

A. Architecture

1. Dataset: Sourced from platforms like Kaggle; images are split 80:20 (train:test).

2. Preprocessing: Cleans data, removes noise and null entries.

3. Feature Extraction: Reduces dimensionality while preserving critical image information.

4. Classification: Trained models (like Random Forest) classify images into categories.

B. System Architecture

• The system uses 70% training and 30% testing data.

• Performance evaluated via accuracy, precision, recall, and F1-score.

C. Implementation

• Tools Used: Python with libraries like NumPy, Pandas, and Scikit-learn.

• Process:

  • Install Python and necessary libraries.

  • Preprocess and split data.

  • Train models and evaluate using performance metrics.

D. Machine Learning Algorithms

1. Random Forest: Robust and efficient ensemble method for classification.

2. VGG16: Performs well generally but lacks fine-grained accuracy for wildlife classification.

3. MobileNet: Lightweight but less accurate in complex scenes or with multiple animals.

4. ResNet50: Most effective in detecting carnivorous animals due to its depth and residual learning capabilities.

IV. Results

The use of deep learning, especially ResNet50, showed promising accuracy for detecting and classifying animals in camera-trap images. However, limitations in models like VGG16 and MobileNet highlight the need for more diverse datasets and fine-tuning.

Conclusion

The image above outlines a structured approach for the carnivorous animal detection project using machine & deep learning pipeline. It begins with dataset division into training and testing set, Classification algorithms are applied to learn distinguishing features of carnivorous animals. The trained model is tasted on unseen data to evaluate its performance. This process ensures reliable and accurate detection of carnivorous animals, making the system effective for real-time or automated wildlife monitoring applications.

References

[1] Abhijeet Singh, MarcinPietarcik, HehlaGhouaiel, Ken BrizelNilanjan Ray. Animal Detection in Man-Made Environments 2020. [2] R. Shanthakumari, C.nalini, S.Vinothkumar, B. Govindraj.Iamge Detection and Recogination of different Species of animals using Deep Learning, April 2022. [3] Z.Zhao et al , “ Object Detection with deep learning: a review , “ IEEE Trans. Neural Netw. Learn. Stse., Vol.30, no 11, pp.32120-3232 ,2019. [4] Harish Kalla, BalachandranRuthramurthy, Satyasis Mishra, GemechuDengia, Sarankumar R. A Pratical Animal Detection and Collision avoidance System Using Deep Learning Model, IEEE July 2022 10.1109/I2CT54291.2022.9824594. [5] Redmon, J., & Farhadi, A., “YOLOv3: An Incremental Improvement,” arXiv preprint arXiv:1804.02767, 2018. [Online]. Available: https://arxiv.org/abs/1804.02767. [6] Goodfellow, I., Bengio, Y., & Courville, A., “Deep learning,” MIT Press, Cambridge, MA, 2016, ISBN: 9780262035613. [7] Szeliski, R., “Computer Vision: Algorithms and Applications,” Springer, 2nd ed., 2021, ISBN: 9783030343729.

Copyright

Copyright © 2025 Aniket Karwa, Mohd Mustafa, Taha Mubashir, Mohd Faizan, Nitu L. Pariyal. 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.