Accelerating industrial vision: Systematic robot-assisted dataset preparation for object detection and pose estimation

The creation of large-scale, high-quality training datasets continues to present a significant challenge for the implementation of artificial intelligence in engineering and industrial robotics. This study introduces a collaborative robot-assisted pipeline that automates data acquisition and annotation, thereby accelerating dataset preparation for object detection and six-degree-of-freedom pose estimation. The proposed system integrates robotic kinematics and image processing to generate vision datasets with multimodal ground-truth labels, such as two-dimensional and three-dimensional bounding boxes, segmentation masks, six-degree-of-freedom poses, and point clouds, within a unified artificial intelligence-driven workflow. To demonstrate the pipeline’s capacity to reduce manual effort and efficiently generate large-scale training datasets for industrial vision applications, an automotive wire harness connector dataset was experimentally prepared using the proposed pipeline. This method achieved annotation speeds approximately 150 times faster than traditional manual techniques and produced high-quality training data for deep learning models. Evaluation with deep learning-based object detection and pose estimation algorithms confirms the effectiveness of the proposed pipeline in preparing datasets for the development of industrial intelligent vision systems. By minimizing human intervention and ensuring systematic viewpoint coverage during dataset preparation, the proposed approach facilitates scalable adoption of artificial intelligence-powered vision systems in industrial automation. The proposed method and code are available at https://github.com/HWANG7308/AutoTrainingDataPrepare .