Araştırma Makalesi

DEVELOPMENT OF A FAULT INJECTION TOOL & DATASET FOR VERIFICATION OF CAMERA BASED PERCEPTION IN ROBOTIC SYSTEMS

Cilt: 30 Sayı: 3 21 Aralık 2022
PDF İndir
TR EN

DEVELOPMENT OF A FAULT INJECTION TOOL & DATASET FOR VERIFICATION OF CAMERA BASED PERCEPTION IN ROBOTIC SYSTEMS

Abstract

Nowadays, camera-based perception is most popular topic in robotic systems. Verification of camera-based perception systems are crucial and difficult with current tools and methods. This study proposes Camera Fault Injection Tool (CamFITool), which enables different kind of fault injection methods to RGB and TOF cameras in order to perform verification and validation activities on robotic systems. Besides, Fault Injected Image Database which is created by CamFITool is introduced. In addition, the study guides to readers to create new datasets by injecting faults into existing image libraries or camera streams with CamFITool. As a result, CamFITool, an open-source fault injection tool, which is a critical tool for assessing of fault tolerant systems’ safety and security, is proposed. Also, a fault injected image dataset created by CamFITool for verification of camera-based perception studies in robotic systems is given.

Keywords

robotics , verification , fault injection , image dataset , camera-based perception

Kaynakça

  1. Referans1 Osadcuks, V., Pudzs, M., Zujevs, A., Pecka, A., & Ardavs, A. (2020, May). Clock-based time sync hronization for an event-based camera dataset acquisition platform. In 2020 IEEE International Conference on Robotics and Automation (ICRA) (pp. 4695-4701). IEEE.
  2. Referans2 Kendall, A., Grimes, M., & Cipolla, R. (2015). Posenet: A convolutional network for real-time 6-dof camera relocalization. In Proceedings of the IEEE international conference on computer vision (pp. 2938-2946).
  3. Referans3 Park, H., & Mu Lee, K. (2017). Joint estimation of camera pose, depth, deblurring, and super-resolution from a blurred image sequence. In Proceedings of the IEEE International Conference on Computer Vision (pp. 4613-4621).
  4. Referans4 Anomaly Detection, A Key Task for AI and Machine Learning, Explained. [Online]. Available: https://www.kdnuggets.com/2019/10/anomaly-detection-explained.html (2021)
  5. Referans5 Scharr, H., Minervini, M., Fischbach, A., & Tsaftaris, S. A. (2014, July). Annotated image datasets of rosette plants. In European Conference on Computer Vision. Zürich, Suisse (pp. 6-12). Referans6 Rezazadegan, F., Shirazi, S., Upcrofit, B., & Milford, M. (2017, May). Action recognition: From static datasets to moving robots. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 3185-3191). IEEE.
  6. Referans7 Su, C., Zhang, S., Xing, J., Gao, W., & Tian, Q. (2016, October). Deep attributes driven multi-camera person re-identification. In European conference on computer vision (pp. 475-491). Springer, Cham.
  7. Referans8 Per, J., Kenk, V. S., Kristan, M., & Kovacic, S. (2012, September). Dana36: A multi-camera image dataset for object identification in surveillance scenarios. In 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance (pp. 64-69). IEEE.
  8. Referans9 Wu, S., Oreifej, O., & Shah, M. (2011, November). Action recognition in videos acquired by a moving camera using motion decomposition of lagrangian particle trajectories. In 2011 International conference on computer vision (pp. 1419-1426). IEEE.
  9. Referans10 Russell, B. C., Torralba, A., Murphy, K. P., & Freeman, W. T. (2008). LabelMe: a database and web-based tool for image annotation. International journal of computer vision, 77(1-3), 157-173.
  10. Referans11 Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009, June). Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition (pp. 248-255). Ieee.

Kaynak Göster

APA
Yayan, U., & Erdoğmuş, A. K. (2022). DEVELOPMENT OF A FAULT INJECTION TOOL & DATASET FOR VERIFICATION OF CAMERA BASED PERCEPTION IN ROBOTIC SYSTEMS. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi, 30(3), 328-339. https://doi.org/10.31796/ogummf.1054761
AMA
1.Yayan U, Erdoğmuş AK. DEVELOPMENT OF A FAULT INJECTION TOOL & DATASET FOR VERIFICATION OF CAMERA BASED PERCEPTION IN ROBOTIC SYSTEMS. ESOGÜ Müh Mim Fak Derg. 2022;30(3):328-339. doi:10.31796/ogummf.1054761
Chicago
Yayan, Uğur, ve Alim Kerem Erdoğmuş. 2022. “DEVELOPMENT OF A FAULT INJECTION TOOL & DATASET FOR VERIFICATION OF CAMERA BASED PERCEPTION IN ROBOTIC SYSTEMS”. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 30 (3): 328-39. https://doi.org/10.31796/ogummf.1054761.
EndNote
Yayan U, Erdoğmuş AK (01 Aralık 2022) DEVELOPMENT OF A FAULT INJECTION TOOL & DATASET FOR VERIFICATION OF CAMERA BASED PERCEPTION IN ROBOTIC SYSTEMS. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 30 3 328–339.
IEEE
[1]U. Yayan ve A. K. Erdoğmuş, “DEVELOPMENT OF A FAULT INJECTION TOOL & DATASET FOR VERIFICATION OF CAMERA BASED PERCEPTION IN ROBOTIC SYSTEMS”, ESOGÜ Müh Mim Fak Derg, c. 30, sy 3, ss. 328–339, Ara. 2022, doi: 10.31796/ogummf.1054761.
ISNAD
Yayan, Uğur - Erdoğmuş, Alim Kerem. “DEVELOPMENT OF A FAULT INJECTION TOOL & DATASET FOR VERIFICATION OF CAMERA BASED PERCEPTION IN ROBOTIC SYSTEMS”. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 30/3 (01 Aralık 2022): 328-339. https://doi.org/10.31796/ogummf.1054761.
JAMA
1.Yayan U, Erdoğmuş AK. DEVELOPMENT OF A FAULT INJECTION TOOL & DATASET FOR VERIFICATION OF CAMERA BASED PERCEPTION IN ROBOTIC SYSTEMS. ESOGÜ Müh Mim Fak Derg. 2022;30:328–339.
MLA
Yayan, Uğur, ve Alim Kerem Erdoğmuş. “DEVELOPMENT OF A FAULT INJECTION TOOL & DATASET FOR VERIFICATION OF CAMERA BASED PERCEPTION IN ROBOTIC SYSTEMS”. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi, c. 30, sy 3, Aralık 2022, ss. 328-39, doi:10.31796/ogummf.1054761.
Vancouver
1.Uğur Yayan, Alim Kerem Erdoğmuş. DEVELOPMENT OF A FAULT INJECTION TOOL & DATASET FOR VERIFICATION OF CAMERA BASED PERCEPTION IN ROBOTIC SYSTEMS. ESOGÜ Müh Mim Fak Derg. 01 Aralık 2022;30(3):328-39. doi:10.31796/ogummf.1054761