@inproceedings{6f973cfa58e7428487175e92292858b7,
title = "PoseNetwork: Pipeline for the Automated Generation of Synthetic Training Data and CNN for Object Detection, Segmentation, and Orientation Estimation",
abstract = "The latest developments and research of convolutional neuronal networks (CNNs) have proven the feasibility of their use in industrial applications that require object detection and pose estimation in unknown environments. Nevertheless, the end-users have neither the required resources for model-training nor the expertise to efficiently implement such applications. On the one hand, our work proposes a pipeline that focuses on the automated generation of training data by using synthetic images. On the other hand, we introduce a deep neural network to estimate the orientation of a reference object by using a one-shot image. We demonstrate the use of PoseNetwork by detecting and estimating the 5D-Pose of a workpiece in a robot-based inspection cell.",
keywords = "CNN for orientation estimation, automated pipeline, one-shot image, synthetic dataset",
author = "Alejandro Maga{\~n}a and Hang Wu and Philipp Bauer and Gunther Reinhart",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 25th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2020 ; Conference date: 08-09-2020 Through 11-09-2020",
year = "2020",
month = sep,
doi = "10.1109/ETFA46521.2020.9212064",
language = "English",
series = "IEEE International Conference on Emerging Technologies and Factory Automation, ETFA",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "587--594",
booktitle = "Proceedings - 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2020",
}