Detection of Endangered Indonesian Species Across Multiple Taxonomic Classes Using Faster R-CNN

Moh. Jabir Mubarok; Rizky Fitria Haya; Eka Fitria; Brilian Surya Budi

doi:10.52436/1.jutif.2025.6.6.4793

Authors

Moh. Jabir Mubarok Electrical Engineering, Institut Teknologi Sepuluh Nopember, Indonesia
Rizky Fitria Haya Informatics, Universitas Syiah Kuala, Indonesia
Eka Fitria Informatics, Universitas Syiah Kuala, Indonesia
Brilian Surya Budi Computer Science, University of Tsukuba, Japan

DOI:

https://doi.org/10.52436/1.jutif.2025.6.6.4793

Keywords:

Deep Learning, Detection, Endangered Species, Faster R-CNN, FPN, Transfer Learning

Abstract

Indonesia’s rich biodiversity includes many endangered species across various taxonomic groups. This study presents a Faster R-CNN deep learning model to detect ten endangered Indonesian species, covering birds, reptiles, mammals, and fishes. A custom dataset with diverse images was annotated and used to train the model with transfer learning on the Detectron2 framework. Evaluation using COCO metrics yielded an average precision (AP) of 54.93%, with the Komodo Dragon achieving the highest AP (82.57%) and Wallace’s Standardwing the lowest (30.82%). The model excels at detecting larger, distinct species but has difficulty with smaller or camouflaged ones in complex environments. Training results confirm that transfer learning aids performance despite limited data. Analysis of misclassifications suggests the need for additional data modalities or context to improve accuracy. This work highlights the potential of Faster R-CNN for automated endangered species monitoring in Indonesia and recommends dataset expansion, data augmentation, and model refinement to enhance detection, particularly for challenging species. This study contributes to computer vision applications in conservation, particularly within low-resource biodiversity contexts.

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