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

References

  1. Ruixu Liu., et al. “Attention mechanism exploits temporal contexts: Real-time 3d human pose reconstruction”. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020).
  2. Zhe Cao., et al. “OpenPose: realtime multi-person 2D pose estimation using Part Affinity Fields”. IEEE Transactions on Pattern Analysis and Machine Intelligence1 (2019): 172-186.
  3. Ruixu Liu., et al. “Enhanced 3d human pose estimation from videos by using attention-based neural network with dilated convolutions”. International Journal of Computer Vision5 (2021): 1596-1615.
  4. Ruixu Liu., et al. “SLAM for robotic navigation by fusing rgb-d and inertial data in recurrent and convolutional neural networks”. 2019 IEEE 5th International Conference on Mechatronics System and Robots (ICMSR). IEEE (2019).
  5. Qingquan Li., et al. “A sensor-fusion drivable-region and lane-detection system for autonomous vehicle navigation in challenging road scenarios”. IEEE Transactions on Vehicular Technology2 (2013): 540-555.
  6. Liu Ruixu and Vijayan K Asari. "3D indoor scene reconstruction and change detection for robotic sensing and navigation”. Mobile Multimedia/Image Processing, Security, and Applications. International Society for Optics and Photonics 10221 (2017).
  7. Liu Ruixu., et al. “3D change detection in staggered voxels model for robotic sensing and navigation”. Mobile Multimedia/Image Processing, Security, and Applications. International Society for Optics and Photonics 9869 (2016).
  8. Aspiras Theus H., et al. “Active Recall Networks for Multiperspectivity Learning through Shared Latent Space Optimization”. IJCCI (2019).
  9. Aspiras Theus., et al. “Convolutional auto-encoder for vehicle detection in aerial imagery (conference presentation)”. Pattern Recognition and Tracking XXX. International Society for Optics and Photonics, 10995 (2019).
  10. Liu Ruixu., et al. “Deep neural network based approach for robust aerial surveillance”. Pattern Recognition and Tracking XXXII. International Society for Optics and Photonics 11735 (2021).
  11. Liu Wei., et al. “SSD: Single shot multibox detector". European Conference on Computer Vision, Springer, Cham (2016).
  12. Redmon Joseph., et al. “You only look once: Unified, real-time object detection". Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016).
  13. Lin Tsung-Yi., et al. “Focal loss for dense object detection". Proceedings of the IEEE International Conference on Computer Vision (2017).
  14. Ren Shaoqing., et al. “Faster RCNN: Towards real-time object detection with region proposal networks". Advances in Neural Information Processing Systems 28 (2015): 91-99.
  15. Sohn Kihyuk., et al. “A simple semi-supervised learning framework for object detection". arXiv preprint arXiv:2005.04757 (2020).
  16. Liu Yen-Cheng., et al. “Unbiased teacher for semi-supervised object detection". arXiv preprint arXiv:2102.09480 (2021).
  17. Su Hao., et al. “Crowdsourcing annotations for visual object detection". Workshops at the Twenty-Sixth AAAI Conference on Artificial Intelligence (2012).
  18. Everingham Mark., et al. “The pascal visual object classes challenge: A retrospective". International Journal of Computer Vision1 (2015): 98-136.
  19. Russakovsky Olga., et al. “ImageNet large scale visual recognition challenge". International Journal of Computer Vision3 (2015): 211-252.
  20. Lin Tsung-Yi., et al. “Microsoft COCO: Common objects in context". European Conference on Computer Vision, Springer, Cham (2014).
  21. Bishwo Adhikari., et al. “Faster Bounding Box Annotation for Object Detection in Indoor Scenes". Proceedings of the 7th European Workshop on Visual Information Processing (EUVIP), July (2018).
  22. Bishwo Adhikari and H Huttunen. "Iterative Bounding Box Annotation for Object Detection". Proceedings of the 25th International Conference on Pattern Recognition (ICPR), July (2020).

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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Acceptance rate35%
Acceptance to publication20-30 days

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