An explainable CNN-based approach for classifying Desert Lions from camera trap images

Jack  Pears; Gelayol Golcarenarenji

An explainable CNN-based approach for classifying Desert Lions from camera trap images

Jack Pears^*, Gelayol Golcarenarenji

^*Corresponding author for this work

School of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The Desert Lion population plays a significant role in promoting tourism and is of considerable ecological interest. Camera traps are used to monitor the lions; however, manually evaluating this data is labor-intensive and time-consuming for conservationists. Hence, this study developed a computer vision system that improves upon the InceptionV3 architecture to classify Desert Lions from camera trap data, with a final accuracy of 97.7 percent on the Desert Lion dataset. The final system is validated on unseen data, achieving an accuracy of 85.9 percent. Grad-CAM was also included for explainability of the model output.

Original language	English
Title of host publication	Proceedings of 2025 5th International Conference on Robotics, Automation, and Artificial Intelligence
Publisher	Institute of Electrical and Electronics Engineers Inc.
Publication status	Accepted for publication - 4 Jun 2025
Event	International Conference Automatics, Robotics & Artificial Intelligence - Faculty of Automatics of the Technical Universiy of Sofia, Sozopol, Bulgaria Duration: 13 Jun 2025 → 15 Jun 2025 https://icarai.tu-sofia.bg/

Conference

Conference	International Conference Automatics, Robotics & Artificial Intelligence
Abbreviated title	ICARAI
Country/Territory	Bulgaria
City	Sozopol
Period	13/06/25 → 15/06/25
Internet address	https://icarai.tu-sofia.bg/

Keywords

Camera trap
Desert lion
Explainability
Wildlife conservation
Deep learning

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Embargoed Document

An explainable CNN-based approach for classifying Desert Lions from camera trap images
Accepted author manuscript (Post-print), 526 KB
Due to publisher’s copyright restrictions, this document is not freely available to download from this website until: 1/01/50

Cite this

@inproceedings{a53d5856da3d438faf8dd929571306db,

title = "An explainable CNN-based approach for classifying Desert Lions from camera trap images",

abstract = "The Desert Lion population plays a significant role in promoting tourism and is of considerable ecological interest. Camera traps are used to monitor the lions; however, manually evaluating this data is labor-intensive and time-consuming for conservationists. Hence, this study developed a computer vision system that improves upon the InceptionV3 architecture to classify Desert Lions from camera trap data, with a final accuracy of 97.7 percent on the Desert Lion dataset. The final system is validated on unseen data, achieving an accuracy of 85.9 percent. Grad-CAM was also included for explainability of the model output. ",

keywords = "Camera trap, Desert lion, Explainability, Wildlife conservation, Deep learning",

author = "Jack Pears and Gelayol Golcarenarenji",

year = "2025",

month = jun,

day = "4",

language = "English",

booktitle = "Proceedings of 2025 5th International Conference on Robotics, Automation, and Artificial Intelligence",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "International Conference Automatics, Robotics \& Artificial Intelligence, ICARAI ; Conference date: 13-06-2025 Through 15-06-2025",

url = "https://icarai.tu-sofia.bg/",

}

Pears, J & Golcarenarenji, G 2025, An explainable CNN-based approach for classifying Desert Lions from camera trap images. in Proceedings of 2025 5th International Conference on Robotics, Automation, and Artificial Intelligence. Institute of Electrical and Electronics Engineers Inc., International Conference Automatics, Robotics & Artificial Intelligence, Sozopol, Bulgaria, 13/06/25.

An explainable CNN-based approach for classifying Desert Lions from camera trap images. / Pears, Jack ; Golcarenarenji, Gelayol.
Proceedings of 2025 5th International Conference on Robotics, Automation, and Artificial Intelligence. Institute of Electrical and Electronics Engineers Inc., 2025.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - An explainable CNN-based approach for classifying Desert Lions from camera trap images

AU - Pears, Jack

AU - Golcarenarenji, Gelayol

PY - 2025/6/4

Y1 - 2025/6/4

N2 - The Desert Lion population plays a significant role in promoting tourism and is of considerable ecological interest. Camera traps are used to monitor the lions; however, manually evaluating this data is labor-intensive and time-consuming for conservationists. Hence, this study developed a computer vision system that improves upon the InceptionV3 architecture to classify Desert Lions from camera trap data, with a final accuracy of 97.7 percent on the Desert Lion dataset. The final system is validated on unseen data, achieving an accuracy of 85.9 percent. Grad-CAM was also included for explainability of the model output.

AB - The Desert Lion population plays a significant role in promoting tourism and is of considerable ecological interest. Camera traps are used to monitor the lions; however, manually evaluating this data is labor-intensive and time-consuming for conservationists. Hence, this study developed a computer vision system that improves upon the InceptionV3 architecture to classify Desert Lions from camera trap data, with a final accuracy of 97.7 percent on the Desert Lion dataset. The final system is validated on unseen data, achieving an accuracy of 85.9 percent. Grad-CAM was also included for explainability of the model output.

KW - Camera trap

KW - Desert lion

KW - Explainability

KW - Wildlife conservation

KW - Deep learning

M3 - Conference contribution

BT - Proceedings of 2025 5th International Conference on Robotics, Automation, and Artificial Intelligence

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - International Conference Automatics, Robotics & Artificial Intelligence

Y2 - 13 June 2025 through 15 June 2025

ER -

An explainable CNN-based approach for classifying Desert Lions from camera trap images

Abstract

Conference

Keywords

UN SDGs

Embargoed Document

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