Acta Scientific Computer Sciences

Research Article Volume 4 Issue 1

Cascaded Semi-supervised Annotation Tool for Image Data Labeling

Ruixu Liu* and Vijayan K Asari

Department of Electrical and Computer Engineering, University of Dayton, USA

*Corresponding Author: Ruixu Liu, Department of Electrical and Computer Engineering, University of Dayton, USA.

Received: November 15, 2021; Published: December 13, 2021

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Abstract

Object detection algorithms have advanced rapidly during the last decade, especially after realizing the efficiency of Convolutional Neural Networks (CNN) for feature extraction. However, it is a time-consuming and labor-intensive task to annotate the objects’ bounding boxes as ground truth data that is necessary to efficiently train the CNN for object detection and recognition tasks. We propose a semi-supervised cascaded bounding box labeling strategy for fast and efficient data annotation for large-scale ground-truth generation. We observed around 80% reduction of workload in data annotation by employing our semi-supervised ground-truth generation method on MS COCO dataset.

Keywords: Object Detection; Bounding Box Labeling; Semi-supervised Method; Data Annotation; Ground-truth Generation

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Citation

Citation: Ruixu Liu and Vijayan K Asari. “Cascaded Semi-supervised Annotation Tool for Image Data Labeling". Acta Scientific Computer Sciences 4.1 (2022): 10-15.

Copyright

Copyright: © 2022 Ruixu Liu and Vijayan K Asari. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




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