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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) |
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Volume 9 | Issue 5
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| Paper Title: | COP : TARGET RECKS USING YOLOv8 |
| Authors Name: | Dishant Prashant Save , Abhishek Mandavkar , Yash Patil , Janhavi Sangoi |
| Download E-Certificate: | Download |
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IJNRD_218758
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| Published Paper Id: | IJNRD2404461 |
| Published In: | Volume 9 Issue 4, April-2024 |
| DOI: | |
| Abstract: | 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. |
| Keywords: | Android, Annotations, CVAT, Detection, Endemic species, YOLOv8. |
| Cite Article: | "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: | 00031 |
| 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 |
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Published Paper ID:IJNRD2404461 Registration ID: 218758 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: e576-e581 Country: Vasai-Virar, Palghar, Maharashtra, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2404461 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2404461 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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