In the past few years, object detection has attracted a lot of attention in the context of human–robot collaboration and Industry 5.0 due to enormous quality improvements in deep learning technologies. In many applications, object detection models have to be able to quickly adapt to a changing environment, i.e., to learn new objects. A crucial but challenging prerequisite for this is the automatic generation of new training data which currently still limits the broad application of object detection methods in industrial manufacturing. In this work, we discuss how to adapt state-of-the-art object detection methods for the task of automatic bounding box annotation in a use case where the background is homogeneous and the object’s label is provided by a human. We compare an adapted version of Faster R-CNN and the Scaled-YOLOv4-p5 architecture and show that both can be trained to distinguish unknown objects from a complex but homogeneous background using only a small amount of training data. In contrast to most other state-of-the-art methods for bounding box labeling, our proposed method neither requires human verification, a predefined set of classes, nor a very large manually annotated dataset. Our method outperforms the state-of-the-art, transformer-based object discovery method LOST on our simple fruits dataset by large margins.
J. Parvanova, V. Alexiev, и S. Kostadinov. International Workshop on Collaborative Annotations in Shared Environment: metadata, vocabularies and techniques in the Digital Humanities (DH-CASE 2013). Collocated with DocEng 2013, Florence, Italy, (сентября 2013)
R. Snow, B. O'Connor, D. Jurafsky, и A. Ng. Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, стр. 254--263. Honolulu, Hawaii, Association for Computational Linguistics, (октября 2008)
M. Sabou, K. Bontcheva, L. Derczynski, и A. Scharl. Proceedings of the Ninth International Conference on Language Resources and Evaluation, ŁREC\ 2014, Reykjavik, Iceland, May 26-31, 2014, стр. 859--866. European Language Resources Association \(ELRA)\, (2014)
B. USADEL, F. POREE, A. NAGEL, M. LOHSE, A. CZEDIK-EYSENBERG, и M. STITT. Plant Cell Environ, 32 (9):
1211-29(2009)Usadel, Bjorn Poree, Fabien Nagel, Axel Lohse, Marc Czedik-Eysenberg, Angelika Stitt, Mark Comparative Study Research Support, Non-U.S. Gov't United States Plant, cell & environment Plant Cell Environ. 2009 Sep;32(9):1211-29. Epub 2009 Mar 24..
D. Weber, A. Voit, G. Kollotzek, и N. Henze. Proceedings of the 18th International Conference on Mobile and Ubiquitous Multimedia, стр. 24:1--24:12. New York, NY, USA, ACM, (2019)
J. Kurhila, M. Miettinen, P. Nokelainen, P. Flor&\#233;en, и H. Tirri. ITiCSE '03: Proceedings of the 8th annual conference on Innovation and technology in computer science education, 35, стр. 173--177. New York, NY, USA, ACM Press, (сентября 2003)
C. Wang, и G. Chen. ITiCSE '04: Proceedings of the 9th annual SIGCSE conference on Innovation and technology in computer science education, 36, стр. 132--136. New York, NY, USA, ACM Press, (сентября 2004)
B. Brush, D. Bargeron, J. Grudin, и A. Gupta. CHI '02: Proceedings of the SIGCHI conference on Human factors in computing systems, стр. 89--96. New York, NY, USA, ACM Press, (2002)