Real-Time On-the-Go Annotation Framework Using YOLO for Automated Dataset Generation

TL;DR

This paper introduces a real-time annotation framework using YOLO models on edge devices, enabling immediate dataset labeling during image capture, which significantly reduces manual effort and accelerates dataset preparation for object detection tasks.

Contribution

The paper proposes a novel real-time annotation system deploying YOLO on edge devices, with comprehensive evaluation of different YOLO architectures and configurations for improved efficiency and accuracy.

Findings

Pretrained YOLO models outperform scratch-trained models in convergence and robustness.

Single-class annotation yields better performance than multi-class configurations.

The framework drastically reduces dataset annotation time while maintaining high quality.

Abstract

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.