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inis 24(1): e4

Research Article

Integrating YOLOv8-agri and DeepSORT for Advanced Motion Detection in Agriculture and Fisheries

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  • @ARTICLE{10.4108/eetinis.v11i1.4618,
        author={Hieu Duong-Trung and Nghia Duong-Trung},
        title={Integrating YOLOv8-agri and DeepSORT for Advanced Motion Detection in Agriculture and Fisheries},
        journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems},
        volume={11},
        number={1},
        publisher={EAI},
        journal_a={INIS},
        year={2024},
        month={2},
        keywords={YOLOv8, DeepSort, Motion Detection, Agricultural Datasets, Reproducibility, Open Data},
        doi={10.4108/eetinis.v11i1.4618}
    }
    
  • Hieu Duong-Trung
    Nghia Duong-Trung
    Year: 2024
    Integrating YOLOv8-agri and DeepSORT for Advanced Motion Detection in Agriculture and Fisheries
    INIS
    EAI
    DOI: 10.4108/eetinis.v11i1.4618
Hieu Duong-Trung1, Nghia Duong-Trung2,*
  • 1: Can Tho University
  • 2: German Research Centre for Artificial Intelligence
*Contact email: duong-trung@ismll.de

Abstract

This paper integrates the YOLOv8-agri models with the DeepSORT algorithm to advance object detection and tracking in the agricultural and fisheries sectors. We address the current limitations in object classification by adapting YOLOv8 to the unique demands of these environments, where misclassification can hinder operational efficiency. Through the strategic use of transfer learning on specialized datasets, our study refines the YOLOv8-agri models for precise recognition and categorization of diverse biological entities. Coupling these models with DeepSORT significantly enhances motion tracking, leading to more accurate and reliable monitoring systems. The research outcomes identify the YOLOv8l-agri model as the optimal solution for balancing detection accuracy with training time, making it highly suitable for precision agriculture and fisheries applications. We have publicly made our experimental datasets and trained models publicly available to foster reproducibility and further research. This initiative marks a step forward in applying sophisticated computer vision techniques to real-world agricultural and fisheries management.

Keywords
YOLOv8, DeepSort, Motion Detection, Agricultural Datasets, Reproducibility, Open Data
Received
2023-12-15
Accepted
2024-02-11
Published
2024-02-12
Publisher
EAI
http://dx.doi.org/10.4108/eetinis.v11i1.4618

Copyright © 2024 H. Duong-Trung et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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