Ocean image data augmentation in the USV virtual training scene

References

• Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., … & Kudlur, M. (2016). Tensorflow: A system for large-scale machine learning. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI’16) (pp. 265–283), Savannah, GA, USA.
   Google Scholar
• Brockman, G., Cheung, V., Pettersson, L., et al., Openai gym[J]. arXiv preprint arXiv:1606.01540, (2016).
   Google Scholar
• Collins, M. J., Denbina, M., & Atteia, G. (2013). On the reconstruction of quad-pol SAR data from compact polarimetry data for ocean target detection. IEEE Transactions on Geoscience and Remote Sensing, 51(1), 591–600.
   Web of Science ®Google Scholar
• Cubuk, E. D., Zoph, B., Mane, D., Vasudevan, V., & Le, Q. V. (2019). Autoaugment: Learning augmentation strategies from data. In Proceedings of the IEEE conference on computer vision and pattern recognition. (pp. 113–123), Long Beach, CA.
   Google Scholar
• Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Li, F. F. (2009). ImageNet: A large-scale hierarchical image database. In Proceeding of IEEE computer vision and pattern recognition (pp. 248–255), Fontainebleau MiamiBeach.
   Google Scholar
• Dong, Z., Wan, L., Li, Y., Liu, T., & Zhang, G. (2015). Trajectory tracking control of underactuated USV based on modified backstepping approach. International Journal of Naval Architecture and Ocean Engineering, 7(5), 817–832.
   Web of Science ®Google Scholar
• Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2015). The Pascal visual object classes challenge: A retrospective. International Journal of Computer Vision, 111(1), 98–136.
   Web of Science ®Google Scholar
• Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010). The Pascal visual object classes (VOC) challenge. International Journal of Computer Vision, 88(2), 303–338.
   Web of Science ®Google Scholar
• Guo, H. (2017a). Big Earth data: A new frontier in Earth and information sciences. Big Earth Data, 1(1–2), 4–20.
   Google Scholar
• Guo, H. (2017b). Big data drives the development of Earth science. Big Earth Data, 1(1–2), 1–3.
   Google Scholar
• Johansen, T. A., Perez, T., & Cristofaro, A. (2016). Ship collision avoidance and COLREGS compliance using simulation-based control behavior selection with predictive hazard assessment. IEEE Transactions on Intelligent Transportation Systems, 17(12), 3407–3422.
   Web of Science ®Google Scholar
• Juliani, A., Berges, V. P., Vckay, E., Gao, Y., Henry, H., Mattar, M., & Lange, D. (2018). Unity: A general platform for intelligent agents. arXiv Prepr. arXiv1809.02627.
   Google Scholar
• Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4, 237–285.
   Web of Science ®Google Scholar
• Kato, S., Tokunaga, S., Maruyama, Y., Maeda, S., & Azumi, T. (2018). Autoware on board: Enabling autonomous vehicles with embedded systems. In 2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS), Porto, Portugal.
   Google Scholar
• Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097–1105), Harrahs and Harveys, Lake Tahoe.
   Google Scholar
• Li, H., Perrie, W., He, Y., Lehner, S., & Brusch, S. (2012). Target detection on the ocean with the relative phase of compact polarimetry SAR. IEEE Transactions on Geoscience and Remote Sensing, 51(6), 3299–3305.
   Web of Science ®Google Scholar
• Lin, T. Y., Maire, M., Belongie, S., et al.,Microsoft coco: Common objects in context[C]//European conference on computer vision. Springer, Cham (2014): 740–755.
   Google Scholar
• Lisowski, J., & Smierzchalski, R. (1970). Methods to assign the safe manoeuvre and trajectory avoiding collision at sea. WIT Transactions on the Built Environment, 12, 12.
   Google Scholar
• Naeem, W., Sutton, R., & Chudley, J. (2006). Modelling and control of an unmanned surface vehicle for environmental monitoring. In UKACC International Control Conference (pp. 1–6), Universities of Glasgow and Strathclyde.
   Google Scholar
• Redmon, J., & Farhadi, A. (2017). YOLO9000: Better, faster, stronger. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7263–7271), Honolulu, Hawaii.
   Google Scholar
• Roberts, G. N., Sutton, R., Zirilli, A., & Tiano, A. (2003). Intelligent ship autopilots—A historical perspective. Mechatronics, 13(10), 1091–1103.
   Web of Science ®Google Scholar
• Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. arXiv Prepr. arXiv1707.06347.
   Google Scholar
• Shah, S., Dey, D., Lovett, C., & Kapoor, A. (2018). AirSim: High-fidelity visual and physical simulation for autonomous vehicles. In Field and service robotics (pp. 621–635). Springer.
   Google Scholar
• Simard, P. Y., Steinkraus, D., Platt, J. C. (2003). Best practices for convolutional neural networks applied to visual document analysis. Icdar, 3, 2003.
   Google Scholar
• Soltan, R. A., Ashrafiuon, H., & Muske, K. R. (2011). ODE-based obstacle avoidance and trajectory planning for unmanned surface vessels. Robotica, 29(5), 691–703.
   Web of Science ®Google Scholar
• Wan, L., Zeiler, M., Zhang, S., Le Cun, Y., & Fergus, R. (2013) Regularization of neural networks using dropconnect. In International conference on machine learning (pp. 1058–1066), Atlanta.
   Google Scholar
• Wang, X., Luo, X., & Liu, H. (2015). Measuring the veracity of web event via uncertainty. Journal of Systems and Software, 102, 226–236.
   Web of Science ®Google Scholar
• Wang, X., Sugumaran, V., Zhang, H., & Xu, Z. (2018). A capability assessment model for emergency management organizations. Information Systems Frontiers, 20(4), 653–667.
   Web of Science ®Google Scholar
• Wang, X., Yuan, S., Zhang, H., Lewis, M., & Sycara, K. (2019). Verbal explanations for deep reinforcement learning neural networks with attention on extracted features. In 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) (pp. 1–7), Le Meridien, Windsor Place, New Delhi, India.
   Google Scholar
• Woodring, J., Petersen, M., Schmeiβer, A., Patchett, J., Ahrens, J., & Hagen, H. (2015). In situ eddy analysis in a high-resolution ocean climate model. IEEE Transactions on Visualization and Computer Graphics, 22(1), 857–866.
   Web of Science ®Google Scholar
• Cheng, Y., & Zhang, W. (2017). Concise deep reinforcement learning obstacle avoidance for underactuated unmanned marine vessels. Neurocomputing, 272, 63–73.
   Web of Science ®Google Scholar
• Zhuo, Y., & Hearn, G. E. (2008). Ship intelligent autopilot in narrow water. In Proceedings of the 27th China control conference, Yun An Conference Resort, Kunming, Yunan Province, China.
   Google Scholar
• Zoph, B., Cubuk, E. D., Ghiasi, G., Lin, T.-Y., Shlens, J., & Le, Q. V. (2019). Learning data augmentation strategies for object detection. arXiv Prepr. arXiv1906.11172.
   Google Scholar