In this study, we propose an automated framework for camel farm monitoring,
introducing two key contributions: the Unified Auto-Annotation framework and
the Fine-Tune Distillation framework. The Unified Auto-Annotation approach
combines two models, GroundingDINO (GD), and Segment-Anything-Model (SAM), to
automatically annotate raw datasets extracted from surveillance videos.
Building upon this foundation, the Fine-Tune Distillation framework conducts
fine-tuning of student models using the auto-annotated dataset. This process
involves transferring knowledge from a large teacher model to a student model,
resembling a variant of Knowledge Distillation. The Fine-Tune Distillation
framework aims to be adaptable to specific use cases, enabling the transfer of
knowledge from the large models to the small models, making it suitable for
domain-specific applications. By leveraging our raw dataset collected from
Al-Marmoom Camel Farm in Dubai, UAE, and a pre-trained teacher model,
GroundingDINO, the Fine-Tune Distillation framework produces a lightweight
deployable model, YOLOv8. This framework demonstrates high performance and
computational efficiency, facilitating efficient real-time object detection.
Our code is available at
\href{https://github.com/Razaimam45/Fine-Tune-Distillation}{this https URL}

本研究提出了一个用于骆驼农场监测的自动化框架，引入了两个关键技术：统一自动注释框架和精调蒸馏框架；统一自动注释方法将 GroundingDINO（GD）和 Segment-Anything-Model（SAM）模型相结合，用于自动标注从监控视频中提取的原始数据集；基于此，精调蒸馏框架通过使用自动标注的数据集对学生模型进行微调，将大型教师模型的知识转移给学生模型；该框架适应特定用例，并能将知识从大型模型传递给小型模型，适用于领域特定应用；借助阿联酋迪拜的 Al-Marmoom 骆驼农场收集的原始数据集和预训练的教师模型 GroundingDINO，精调蒸馏框架生成一种轻量级的可部署模型 YOLOv8；这一框架表现出高性能和计算效率，有助于实现高效的实时目标检测。