Efficient and accurate annotation of datasets remains a significant challenge for deploying object detection models such as You Only Look Once (YOLO) in real-world applications, particularly in agriculture where rapid decision-making is critical. Traditional annotation techniques are labor-intensive, requiring extensive manual labeling post data collection. This paper presents a novel real-time annotation approach leveraging YOLO models deployed on edge devices, enabling immediate labeling during image capture. To comprehensively evaluate the efficiency and accuracy of our proposed system, we conducted an extensive comparative analysis using three prominent YOLO architectures (YOLOv5, YOLOv8, YOLOv12) under various configurations: single-class versus multi-class annotation and pretrained versus scratch-based training. Our analysis includes detailed statistical tests and learning dynamics, demonstrating significant advantages of pretrained and single-class configurations in terms of model convergence, performance, and robustness. Results strongly validate the feasibility and effectiveness of our real-time annotation framework, highlighting its capability to drastically reduce dataset preparation time while maintaining high annotation quality.

翻译：高效且准确的数据集标注仍然是部署如You Only Look Once (YOLO)等目标检测模型于实际应用中的重大挑战，尤其是在农业等需要快速决策的关键领域。传统标注方法劳动密集，需要在数据收集后进行大量人工标记。本文提出了一种新颖的实时标注方法，利用部署在边缘设备上的YOLO模型，实现在图像捕获过程中即时标注。为全面评估所提系统的效率与准确性，我们使用三种主流YOLO架构（YOLOv5、YOLOv8、YOLOv12）在不同配置下进行了广泛的对比分析：单类别与多类别标注，以及预训练与从零开始训练。我们的分析包括详细的统计检验和学习动态研究，证明了预训练和单类别配置在模型收敛性、性能和鲁棒性方面的显著优势。结果充分验证了我们实时标注框架的可行性和有效性，突显了其在保持高标注质量的同时，能够大幅缩短数据集准备时间的能力。