WildEye: Sistema de reconocimiento y rescate de animales en peligro de extinción mediante aprendizaje profundo

Alejandro Corro Sabino

doi:10.22490/25394088.10444

Vol. 20 No. 1 (2026)

Published 2026-02-23

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Article

WildEye: System for Recognizing and Rescuing Endangered Animals Using Deep Learning

DOI: https://doi.org/10.22490/25394088.10444

Alejandro Corro Sabino Simón Bolívar University

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Abstract
References

WildEye proposes a web platform that integrates an image classifier based on convolutional neural networks (CNN) with a reporting management workflow to facilitate the identification and rescue of potentially endangered animals. The system allows users to upload images, retrieves the geographic location of the image from metadata, classifies the specimen, and assigns the report to the authorities or corporations responsible for the corresponding department and country. The model was implemented with TensorFlow/Keras and trained with a dataset of more than 5,000 images distributed across 90 categories, achieving a reported accuracy of ~99 % in training and ~90 % in testing after 35 epochs (loss of 1 %). The article presents the methodology for data collection and preprocessing, the training procedure and the evaluation metrics, discusses limitations (e.g., lack of explicit endangered species labeling in the dataset) and proposes future lines of work (expansion of the dataset with conservation status labels, field validation, and adoption of detection/segmentation models to strengthen identification). The results highlight the potential of integrating deep learning and collaborative platforms to accelerate responses to sightings and support conservation efforts.

keywords: Deep Learning, Convolutional neural networks, Species recognition, Wildlife rescue, Collaborative platform

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How to Cite

Corro Sabino, A. (2026). WildEye: System for Recognizing and Rescuing Endangered Animals Using Deep Learning. Publicaciones E Investigación, 20(1). https://doi.org/10.22490/25394088.10444

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