INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
With the increasing need for effective wildlife monitoring and conservation efforts, computer vision technologies have
emerged as powerful tools for automating animal detection in diverse environments. This paper introduces an innovative framework
for the detection of Indian exclusive animals—species found exclusively in India—employing the YOLOv8 (You Only Look One level) object detection model. The proposed system is reinforced by a meticulously annotated dataset created through the Computer
Vision Annotation Tool (CVAT), focusing specifically on the distinctive fauna inhabiting the Indian subcontinent. The YOLOv8
model, renowned for its speed and accuracy, is employed to detect animals in images and video frames. The YOLOv8 model is
tailored to detect and classify indigenous animal species, ensuring its adaptability to the unique ecological contexts of India. By
harnessing the real-time capabilities of YOLOv8, the system enables efficient and timely monitoring of exclusive wildlife
populations, addressing the urgent need for accurate and scalable solutions in conservation efforts. The CVAT annotated dataset
encapsulates a diverse array of Indian endemic species, encompassing various habitats and environmental conditions. The manual
annotation process ensures precision in delineating bounding boxes around animals, contributing significantly to the enhancement
of the model's detection accuracy for region-specific fauna. Addressing challenges such as diverse animal poses, complex
backgrounds, and varying lighting conditions, our framework demonstrates its adaptability to the specific conditions prevalent in
India. This work contributes to the growing body of research in wildlife conservation and monitoring, providing a scalable and
accurate solution for automated animal detection. The proposed framework stands as a valuable tool for researchers,
conservationists, and wildlife managers dedicated to safeguarding the unique biodiversity of India and its integral role in global
ecological balance.
"COP : TARGET RECKS USING YOLOv8", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.e576-e581, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404461.pdf
Downloads:
00033
ISSN:
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Facebook Twitter Instagram LinkedIn