Abstract
This paper introduces a novel physical annotation system designed to generate training data for automated optical inspection. The system uses pointer-based in-situ interaction to transfer the valuable expertise of trained inspection personnel directly into a machine learning (ML) training pipeline. Unlike conventional screen-based annotation methods, our system captures physical trajectories and contours directly on the object, providing a more intuitive and efficient way to label data. The core technology uses calibrated, tracked pointers to accurately record user input and transform these spatial interactions into standardised annotation formats that are compatible with open-source annotation software. Additionally, a simple projector-based interface projects visual guidance onto the object to assist users during the annotation process, ensuring greater accuracy and consistency. The proposed concept bridges the gap between human expertise and automated data generation, enabling non-IT experts to contribute to the ML training pipeline and preventing the loss of valuable training samples. Preliminary evaluation results confirm the feasibility of capturing detailed annotation trajectories and demonstrate that integration with CVAT streamlines the workflow for subsequent ML tasks. This paper details the system architecture, calibration procedures and interface design, and discusses its potential contribution to future ML data generation for automated optical inspection.
Abstract (translated)
本文介绍了一种新颖的物理标注系统，旨在为自动化光学检测生成训练数据。该系统使用基于指针的现场互动方法，将受过培训的操作人员的专业知识直接转化为机器学习（ML）训练管道的一部分。与传统的屏幕式注释方法不同，我们的系统可以直接在物体上捕捉物理轨迹和轮廓，提供一种更直观且高效的标注方式。 核心技术采用校准、跟踪指针来准确记录用户输入，并将这些空间互动转换为与开源注释软件兼容的标准注释格式。此外，一个简单的投影仪界面将视觉指导直接投射到物体表面，帮助用户在注释过程中提高准确性并保持一致性。所提出的概念弥合了人类专业知识和自动数据生成之间的差距，使非IT专家也能参与到ML训练管道中，并防止有价值的训练样本丢失。 初步评估结果证实了捕捉详细标注轨迹的可行性，并展示了与CVAT集成可以简化后续ML任务的工作流程。本文详细介绍了系统架构、校准程序和界面设计，并讨论了其对未来自动化光学检测中的ML数据生成的潜在贡献。
URL
https://arxiv.org/abs/2506.05026
