Publication
Object Detection for Human-Robot Interaction and Worker Assistance Systems
Hooman Tavakoli; Sungho Suh; Snehal Walunj; Parsha Pahlevannejad; Christiane Plociennik; Martin Ruskowski
In: John Soldatos. Artificial Intelligence in Manufacturing: Enabling Intelligent, Flexible and Cost-Effective Production Through AI. Pages 319-332, ISBN 978-3-031-46452-2, Springer Nature Switzerland, Cham, 2024.
Abstract
The primary goal of this research is to describe the scenarios, challenges, and complexities associated with object detection in industrial environments and to provide clues on how to tackle them. While object detection in production lines offers significant advantages, it also poses notable difficulties. This chapter delves into the common scenarios and specific challenges encountered in industrial object detection and proposes targeted solutions for various use cases. For example, synthetic data play a pivotal role in overcoming labeling challenges, particularly when it comes to small objects. By harnessing synthetic data, we can efficiently track and debug object detection results, ensuring faster identification and resolution of many data labeling issues. Synthetic data facilitate effective tracking and debugging of object detection results, streamlining the overall workflow. Furthermore, we explore the application of object detection in head-worn devices, utilizing the human point of view (POV) as a valuable perspective. This approach not only enhances human assistance systems but also enhances safety in specific use cases. Through this research endeavor, our aim is to contribute to the advancement of the whole process of object detection methods in complex industrial environments.
Projekte
InCoRAP - Intentionsbasierte kooperative Roboterhandlungsplanung und Werkerunterstützung in Fabrikumgebungen,
STAR - Safe and Trusted Human Centric ARtificial Intelligence in Future Manufacturing Lines
STAR - Safe and Trusted Human Centric ARtificial Intelligence in Future Manufacturing Lines