Object pose estimation is a core perception task that enables, for example, object
manipulation and scene understanding. The widely available, inexpensive, and high …

Object detection (OD) networks trained with CAD-based synthetic datasets still face
significant challenges in detecting real mechanical parts in heavily cluttered industry scenes …

Recent problems in robotics can sometimes only be tackled using machine learning
technologies, particularly those that utilize deep learning (DL) with transfer learning …

Deep learning-based methods have demonstrated remarkable success in object detection
tasks when abundant training data are available. However, in the industrial domain …

Deep learning methods have recently exhibited impressive performance in object detection.
However, such methods needed much training data to achieve high recognition accuracy …

Domain adaptation (DA) is essential for developing robust machine learning models
capable of operating across different domains with minimal retraining. This study explores …

In the field of robotic grasping, 2D pose estimation algorithms are outdated and insufficient
for modern requirements. Transitioning to 6D pose estimation of objects offers, particularly …

Augmented reality tracking is the core problem of augmented reality, which has not been
well resolved so far, and limits the wider application of industrial augmented reality in actual …

Generating synthetic data is a promising solution to the challenge of limited training data for
industrial deep learning applications. However, training on synthetic data and testing on real …

This paper explores a data augmentation approach for images of rigid bodies, particularly
focusing on electrical equipment and analogous industrial objects. By leveraging …