整合 YOLOv8-agri 和 DeepSORT,实现农业和渔业领域的高级运动检测

Q2 Engineering
Hieu Duong-Trung, Nghia Duong-Trung
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引用次数: 0

摘要

本文将 YOLOv8-agri 模型与 DeepSORT 算法相结合,以推进农业和渔业领域的物体检测和跟踪。我们通过调整 YOLOv8 来适应这些环境的独特需求,从而解决目前在物体分类方面存在的局限性。通过在专门数据集上战略性地使用迁移学习,我们的研究完善了 YOLOv8-agri 模型,以实现对各种生物实体的精确识别和分类。将这些模型与 DeepSORT 相结合,可显著增强运动跟踪能力,从而开发出更准确、更可靠的监控系统。研究结果表明,YOLOv8l-agri 模型是兼顾检测精度和训练时间的最佳解决方案,因此非常适合精准农业和渔业应用。我们公开了实验数据集和训练模型,以促进可重复性和进一步研究。这一举措标志着我们在将复杂的计算机视觉技术应用于现实世界的农业和渔业管理方面又向前迈进了一步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrating YOLOv8-agri and DeepSORT for Advanced Motion Detection in Agriculture and Fisheries
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.
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CiteScore
4.00
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0.00%
发文量
15
审稿时长
10 weeks
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