DEVELOPMENT OF A FAULT INJECTION TOOL & DATASET FOR VERIFICATION OF CAMERA BASED PERCEPTION IN ROBOTIC SYSTEMS
Yıl 2022,
Cilt: 30 Sayı: 3, 328 - 339, 21.12.2022
Uğur Yayan
,
Alim Kerem Erdoğmuş
Öz
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.
Destekleyen Kurum
ECSEL Joint Undertaking (JU) ve TÜBİTAK
Proje Numarası
876852 ve 120N803
Kaynakça
- 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.
- 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).
- 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).
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Anomaly Detection, A Key Task for AI and Machine Learning, Explained. [Online]. Available: https://www.kdnuggets.com/2019/10/anomaly-detection-explained.html (2021)
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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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Referans12
Everingham, M., Van Gool, L., Williams, C. K., Winn, J., & Zisserman, A. (2010). The pascal visual object classes (voc) challenge. International journal of computer vision, 88(2), 303-338.
- Referans13
Torralba, A., Fergus, R., & Freeman, W. T. (2008). 80 million tiny images: A large data set for nonparametric object and scene recognition. IEEE transactions on pattern analysis and machine intelligence, 30(11), 1958-1970.
- Referans14
Noguchi, A., & Harada, T. (2019). Rgbd-gan: Unsupervised 3d representation learning from natural image datasets via rgbd image synthesis. arXiv preprint arXiv:1909.12573.
- Referans15
Leitner, J., Dansereau, D., Shirazi, S., & Corke, P. (2015). The need for dynamic and active datasets. In CVPR Workshop on The Future of Datasets in Computer Vision (pp. 1-1).
- Referans16
Orchard, G., Jayawant, A., Cohen, G. K., & Thakor, N. (2015). Converting static image datasets to spiking neuromorphic datasets using saccades. Frontiers in neuroscience, 9, 437.
- Referans17
Ravi, N., Shankar, P., Frankel, A., Elgammal, A., & Iftode, L. (2005, August). Indoor localization using camera phones. In Seventh IEEE Workshop on Mobile Computing Systems & Applications (WMCSA'06 Supplement) (pp. 1-7). IEEE.
- Referans18
Padhy, R. P., Verma, S., Ahmad, S., Choudhury, S. K., & Sa, P. K. (2018). Deep neural network for autonomous uav navigation in indoor corridor environments. Procedia computer science, 133, 643-650.
- Referans19
Gloe, T., & Böhme, R. (2010, March). The'Dresden Image Database'for benchmarking digital image forensics. In Proceedings of the 2010 ACM Symposium on Applied Computing (pp. 1584-1590).
- Referans20
Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., ... & Ng, A. Y. (2009, May). ROS: an open-source Robot Operating System. In ICRA workshop on open source software (Vol. 3, No. 3.2, p. 5).
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GAZEBO website. [Online]. Available: http://GAZEBOsim.org/, (2021)
- Referans22
Chitta, S., Sucan, I., & Cousins, S. (2012). Moveit![ros topics]. IEEE Robotics & Automation Magazine, 19(1), 18-19.
- Referans23
Sucan, I. A., Moll, M., & Kavraki, L. E. (2012). The open motion planning library. IEEE Robotics & Automation Magazine, 19(4), 72-82.
- Referans24
Open Source Computer Vision, OpenCV-Python Tutorials, Morphological Transformations. [Online]. Available: https://docs.opencv.org/4.5.3/d9/d61/tutorial_py_morphological_ops.html, (2021)
- Referans25
Nene, S. A., Nayar, S. K., & Murase, H. (1996). Columbia object image library (coil-100).
- Referans26
Xiao, J., Hays, J., Ehinger, K. A., Oliva, A., & Torralba, A. (2010, June). Sun database: Large-scale scene recognition from abbey to zoo. In 2010 IEEE computer society conference on computer vision and pattern recognition (pp. 3485-3492). IEEE.
- Referans27
Fregin, A., Muller, J., Krebel, U., & Dietmayer, K. (2018, May). The DriveU traffic light dataset: Introduction and comparison with existing datasets. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 3376-3383). IEEE.
- Referans28
Barbu, T. (2013, December). Variational image denoising approach with diffusion porous media flow. In Abstract and Applied Analysis (Vol. 2013). Hindawi.
- Referans29
Schottky, W. (2018). On spontaneous current fluctuations in various electrical conductors. Journal of Micro/Nanolithography, MEMS, and MOEMS, 17(4), 041001.
- Referans30
Blanter, Y. M., & Büttiker, M. (2000). Shot noise in mesoscopic conductors. Physics reports, 336(1-2), 1-166.
- Referans31
Rosin, P., & Collomosse, J. (Eds.). (2012). Image and video-based artistic stylisation (Vol. 42). Springer Science & Business Media.
- Referans32
Erdogmus, A. K., & Karaca, M. (2021). Manipulation of Camera Sensor Data via Fault Injection for Anomaly Detection Studies in Verification and Validation Activities For AI. arXiv preprint arXiv:2108.13803.
- Referans33
Hsueh, M. C., Tsai, T. K., & Iyer, R. K. (1997). Fault injection techniques and tools. Computer, 30(4), 75-82.
- Referans34
Parasyris, K., Tziantzoulis, G., Antonopoulos, C. D., & Bellas, N. (2014, June). GemFI: A fault injection tool for studying the behavior of applications on unreliable substrates. In 2014 44th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (pp. 622-629). IEEE.
- Referans35
Aidemark, J., Vinter, J., Folkesson, P., & Karlsson, J. (2001, July). Goofi: Generic object-oriented fault injection tool. In 2001 International Conference on Dependable Systems and Networks (pp. 83-88). IEEE.
- Referans36
Hari, S. K. S., Tsai, T., Stephenson, M., Keckler, S. W., & Emer, J. (2017, April). Sassifi: An architecture-level fault injection tool for gpu application resilience evaluation. In 2017 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS) (pp. 249-258). IEEE.
- Referans37
Svenningsson, R., Vinter, J., Eriksson, H., & Törngren, M. (2010, September). MODIFI: a MODel-implemented fault injection tool. In International Conference on Computer Safety, Reliability, and Security (pp. 210-222). Springer, Berlin, Heidelberg.
- Referans38
Yayan, U. & Erdoğmuş, A. (2021). Endüstriyel Robot Hareket Planlama Algoritmaları Performans Karşılaştırması . Journal of Scientific, Technology and Engineering Research , 2 (2) , 31-45 . DOI: 10.53525/jster.979689
- Referans39
Camera Fault Injection Tool, Inovasyon Muhendislik Github Repository, (2021), https://github.com/inomuh/Camera-Fault-Injection-Tool
- Referans40
IFR International Federation of Robotics, (2021), https://ifr.org/ifr-press-releases/news/robot-sales-rise-again
- Referans41
Camera Fault Injection Tool, ROS Wiki, (2021), wiki.ros.org/CamFITool
- Referans42
Jankowski, M. (2006). Erosion, dilation and related operators. Department of Electrical EngineeringUniversity of Southern Maine Portland, Maine, USA.
- Referans43
Acton, S. T., & Mukherjee, D. P. (2000). Scale space classification using area morphology. IEEE Transactions on Image Processing, 9(4), 623-635.
- Referans44
Larnier, S., Fehrenbach, J., & Masmoudi, M. (2012). The topological gradient method: From optimal design to image processing. Milan Journal of Mathematics, 80(2), 411-441.
- Referans45
Ji, H., & Liu, C. (2008, June). Motion blur identification from image gradients. In 2008 IEEE Conference on Computer Vision and Pattern Recognition (pp. 1-8). IEEE.
ROBOTİK SİSTEMLERDE KAMERA TABANLI ALGININ DOĞRULANMASI İÇİN HATA ENJEKSİYON ARACI VE VERİ KÜMESİNİN GELİŞTİRİLMESİ
Yıl 2022,
Cilt: 30 Sayı: 3, 328 - 339, 21.12.2022
Uğur Yayan
,
Alim Kerem Erdoğmuş
Öz
Günümüzde robotik sistemlerde kamera tabanlı algılama en popüler konulardan biridir. Mevcut araç ve yöntemlerle kamera tabanlı algılama sistemlerinin doğrulanması da çok önemli ve zordur. Bu çalışma, robotik sistemlerde doğrulama ve onaylama faaliyetlerini gerçekleştirmek için RGB ve TOF kameralara farklı türlerde hata enjeksiyon yöntemleri sağlayan Kamera Hatası Enjeksiyon Aracını (CamFITool) önermektedir. Ayrıca CamFITool tarafından oluşturulan hata enjekte edilmiş imge kümesi tanıtılmaktadır. Buna ek olarak çalışma, CamFITool ile mevcut görüntü kitaplıklarına veya kamera akışlarına hatalar enjekte ederek yeni imge kümeleri oluşturmak için okuyuculara rehberlik edilmektedir. Sonuç olarak, hataya dayanıklı sistemlerin emniyet ve güvenliğini değerlendirmek için kritik bir araç olan açık kaynaklı hata enjeksiyon aracı CamFITool önerilmiştir. Ayrıca robotik sistemlerde kamera tabanlı algılama çalışmalarının doğrulanması için CamFITool tarafından oluşturulan hata enjekte edilmiş görüntü veri kümesi verilmiştir.
Proje Numarası
876852 ve 120N803
Kaynakça
- 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.
- 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).
- 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).
- 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)
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Referans12
Everingham, M., Van Gool, L., Williams, C. K., Winn, J., & Zisserman, A. (2010). The pascal visual object classes (voc) challenge. International journal of computer vision, 88(2), 303-338.
- Referans13
Torralba, A., Fergus, R., & Freeman, W. T. (2008). 80 million tiny images: A large data set for nonparametric object and scene recognition. IEEE transactions on pattern analysis and machine intelligence, 30(11), 1958-1970.
- Referans14
Noguchi, A., & Harada, T. (2019). Rgbd-gan: Unsupervised 3d representation learning from natural image datasets via rgbd image synthesis. arXiv preprint arXiv:1909.12573.
- Referans15
Leitner, J., Dansereau, D., Shirazi, S., & Corke, P. (2015). The need for dynamic and active datasets. In CVPR Workshop on The Future of Datasets in Computer Vision (pp. 1-1).
- Referans16
Orchard, G., Jayawant, A., Cohen, G. K., & Thakor, N. (2015). Converting static image datasets to spiking neuromorphic datasets using saccades. Frontiers in neuroscience, 9, 437.
- Referans17
Ravi, N., Shankar, P., Frankel, A., Elgammal, A., & Iftode, L. (2005, August). Indoor localization using camera phones. In Seventh IEEE Workshop on Mobile Computing Systems & Applications (WMCSA'06 Supplement) (pp. 1-7). IEEE.
- Referans18
Padhy, R. P., Verma, S., Ahmad, S., Choudhury, S. K., & Sa, P. K. (2018). Deep neural network for autonomous uav navigation in indoor corridor environments. Procedia computer science, 133, 643-650.
- Referans19
Gloe, T., & Böhme, R. (2010, March). The'Dresden Image Database'for benchmarking digital image forensics. In Proceedings of the 2010 ACM Symposium on Applied Computing (pp. 1584-1590).
- Referans20
Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., ... & Ng, A. Y. (2009, May). ROS: an open-source Robot Operating System. In ICRA workshop on open source software (Vol. 3, No. 3.2, p. 5).
- Referans21
GAZEBO website. [Online]. Available: http://GAZEBOsim.org/, (2021)
- Referans22
Chitta, S., Sucan, I., & Cousins, S. (2012). Moveit![ros topics]. IEEE Robotics & Automation Magazine, 19(1), 18-19.
- Referans23
Sucan, I. A., Moll, M., & Kavraki, L. E. (2012). The open motion planning library. IEEE Robotics & Automation Magazine, 19(4), 72-82.
- Referans24
Open Source Computer Vision, OpenCV-Python Tutorials, Morphological Transformations. [Online]. Available: https://docs.opencv.org/4.5.3/d9/d61/tutorial_py_morphological_ops.html, (2021)
- Referans25
Nene, S. A., Nayar, S. K., & Murase, H. (1996). Columbia object image library (coil-100).
- Referans26
Xiao, J., Hays, J., Ehinger, K. A., Oliva, A., & Torralba, A. (2010, June). Sun database: Large-scale scene recognition from abbey to zoo. In 2010 IEEE computer society conference on computer vision and pattern recognition (pp. 3485-3492). IEEE.
- Referans27
Fregin, A., Muller, J., Krebel, U., & Dietmayer, K. (2018, May). The DriveU traffic light dataset: Introduction and comparison with existing datasets. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 3376-3383). IEEE.
- Referans28
Barbu, T. (2013, December). Variational image denoising approach with diffusion porous media flow. In Abstract and Applied Analysis (Vol. 2013). Hindawi.
- Referans29
Schottky, W. (2018). On spontaneous current fluctuations in various electrical conductors. Journal of Micro/Nanolithography, MEMS, and MOEMS, 17(4), 041001.
- Referans30
Blanter, Y. M., & Büttiker, M. (2000). Shot noise in mesoscopic conductors. Physics reports, 336(1-2), 1-166.
- Referans31
Rosin, P., & Collomosse, J. (Eds.). (2012). Image and video-based artistic stylisation (Vol. 42). Springer Science & Business Media.
- Referans32
Erdogmus, A. K., & Karaca, M. (2021). Manipulation of Camera Sensor Data via Fault Injection for Anomaly Detection Studies in Verification and Validation Activities For AI. arXiv preprint arXiv:2108.13803.
- Referans33
Hsueh, M. C., Tsai, T. K., & Iyer, R. K. (1997). Fault injection techniques and tools. Computer, 30(4), 75-82.
- Referans34
Parasyris, K., Tziantzoulis, G., Antonopoulos, C. D., & Bellas, N. (2014, June). GemFI: A fault injection tool for studying the behavior of applications on unreliable substrates. In 2014 44th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (pp. 622-629). IEEE.
- Referans35
Aidemark, J., Vinter, J., Folkesson, P., & Karlsson, J. (2001, July). Goofi: Generic object-oriented fault injection tool. In 2001 International Conference on Dependable Systems and Networks (pp. 83-88). IEEE.
- Referans36
Hari, S. K. S., Tsai, T., Stephenson, M., Keckler, S. W., & Emer, J. (2017, April). Sassifi: An architecture-level fault injection tool for gpu application resilience evaluation. In 2017 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS) (pp. 249-258). IEEE.
- Referans37
Svenningsson, R., Vinter, J., Eriksson, H., & Törngren, M. (2010, September). MODIFI: a MODel-implemented fault injection tool. In International Conference on Computer Safety, Reliability, and Security (pp. 210-222). Springer, Berlin, Heidelberg.
- Referans38
Yayan, U. & Erdoğmuş, A. (2021). Endüstriyel Robot Hareket Planlama Algoritmaları Performans Karşılaştırması . Journal of Scientific, Technology and Engineering Research , 2 (2) , 31-45 . DOI: 10.53525/jster.979689
- Referans39
Camera Fault Injection Tool, Inovasyon Muhendislik Github Repository, (2021), https://github.com/inomuh/Camera-Fault-Injection-Tool
- Referans40
IFR International Federation of Robotics, (2021), https://ifr.org/ifr-press-releases/news/robot-sales-rise-again
- Referans41
Camera Fault Injection Tool, ROS Wiki, (2021), wiki.ros.org/CamFITool
- Referans42
Jankowski, M. (2006). Erosion, dilation and related operators. Department of Electrical EngineeringUniversity of Southern Maine Portland, Maine, USA.
- Referans43
Acton, S. T., & Mukherjee, D. P. (2000). Scale space classification using area morphology. IEEE Transactions on Image Processing, 9(4), 623-635.
- Referans44
Larnier, S., Fehrenbach, J., & Masmoudi, M. (2012). The topological gradient method: From optimal design to image processing. Milan Journal of Mathematics, 80(2), 411-441.
- Referans45
Ji, H., & Liu, C. (2008, June). Motion blur identification from image gradients. In 2008 IEEE Conference on Computer Vision and Pattern Recognition (pp. 1-8). IEEE.