Automated object detection has become increasingly valuable across diverse applications, yet efficient, high-quality annotation remains a persistent challenge. In this paper, we present the development and evaluation of a platform designed to interactively improve object detection models. The platform allows uploading and annotating images as well as fine-tuning object detection models. Users can then manually review and refine annotations, further creating improved snapshots that are used for automatic object detection on subsequent image uploads - a process we refer to as semi-automatic annotation resulting in a significant gain in annotation efficiency. Whereas iterative refinement of model results to speed up annotation has become common practice, we are the first to quantitatively evaluate its benefits with respect to time, effort, and interaction savings. Our experimental results show clear evidence for a significant time reduction of up to 53% for semi-automatic compared to manual annotation. Importantly, these efficiency gains did not compromise annotation quality, while matching or occasionally even exceeding the accuracy of manual annotations. These findings demonstrate the potential of our lightweight annotation platform for creating high-quality object detection datasets and provide best practices to guide future development of annotation platforms. The platform is open-source, with the frontend and backend repositories available on GitHub.

翻译：自动化目标检测在各类应用中日益重要，然而高效、高质量的标注仍是持续存在的挑战。本文介绍了一个旨在交互式改进目标检测模型的平台的开发与评估。该平台支持上传和标注图像，并能对目标检测模型进行微调。用户可以手动审查并优化标注，进而创建改进后的模型快照，用于后续上传图像的自动目标检测——这一过程我们称为半自动标注，可显著提升标注效率。尽管通过迭代优化模型结果以加速标注已成为常见做法，但我们是首个从时间、精力和交互节省角度定量评估其效益的研究。实验结果表明，与手动标注相比，半自动标注可显著减少高达53%的时间。重要的是，这些效率提升并未损害标注质量，同时达到甚至偶尔超越了手动标注的准确度。这些发现证明了我们轻量级标注平台在创建高质量目标检测数据集方面的潜力，并为未来标注平台的开发提供了最佳实践指导。该平台为开源项目，前端和后端代码库均可在GitHub上获取。