Wild Animal Facing Extinction

Published: 24 February 2023| Version 2 | DOI: 10.17632/vhmvfbgvxj.2
Sibusiso Reuben Bakana


Wildlife conservation is an essential matter, particularly in Africa, where hunting and poaching constitute an important danger to rhinos and elephants. Despite the efforts of governments and NGOs, the scarcity of annotated wild animal datasets has hindered the development of computer algorithms for monitoring and protecting these animals in natural reserves and national parks. While advances in technology have enabled institutions to upload images for monitoring purposes, manual annotation remains time-consuming and inefficient. To address this issue, the paper proposes a semi-automatic annotation framework for wild animal dataset construction, focusing on six classes (rhino, elephant, lion, giraffe, cheetah, and zebra). The framework involves training a model on a small manual dataset and using this to semi-automatically label the remaining large-scale dataset. Additionally, the authors manually annotated rhino images with skeletons to be expropriated in computer vision algorithms for pose estimation tasks. This is the first wild animal dataset constructed with boundary box labels based on semi-automatic annotation and rhino skeletons manually annotated. The proposed framework has the potential to expedite research in wildlife protection, serve as a guide for semi-automatic annotation dataset construction, and possess a significant impact on the conservation community. Our dataset is publicly available here: Keywords: Wild animal dataset, semi-automatic annotation, deep learning algorithms, boundary box labels, rhino skeleton, time efficient Wild Animals Boundary Box Labels Class Number of Images Train Valid Test Rhino 1389 6107 763 764 Cheetah 1150 Elephant 1288 Lion 1277 Zebra 1321 Giraffe 1209 Total 7634 Image = .jpg & Annotation = .txt Rhino Skeleton Labels Class Number of Images Train Valid Test Rhino Skeleton 1410 1048 262 100 Total 1410 Image = .jpg & Annotation = .json NB: The Rhino Skeleton dataset was annotated manually on this site V7:V7.https://www.v7labs.com and can be separately cited as below: Citation - Video Surveillance based Wild Animal Poaching Mitigation (2023).Rhino Skeleton. BAKANA SIBUSISO REUBEN, V7 Open Datasets. https://darwin.v7labs.com/video-surveillance-based-wild-animal-poaching-mitigation/rhino-skeleton Also is it publicly available at: https://darwin.v7labs.com/video-surveillance-based-wild-animal-poaching-mitigation/rhino-skeleton



Beihang University


Computer Vision, Annotation, Object Detection, Object Recognition, Image Classification, Conservation Management, Conservation of Biodiversity, Point Estimation, Wildlife Conservation, Wildlife, Skeleton