COP : TARGET RECKS USING YOLOv8


EOI: 10.11242/viva-tech.01.06.020

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Citation

Abhishek Mandavkar, Dishant Save, Yash Patil, Prof. Janhavi Sangoi, "COP : TARGET RECKS USING YOLOv8", VIVA-IJRI Volume 1, Issue 6, Article 20, pp. 1-8, 2023. Published by Computer Engineering Department, VIVA Institute of Technology, Virar, India.

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.

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