Pulse-coupled neural network performance for real-time identification of vegetation during forced landing

Warne, David James ; Hayward, Ross ; Kelson, Neil ; Banks, Jasmine ; Mejias, Luis

ANZIAM Journal, Tome 55 (2014), / Harvested from Australian Mathematical Society

Résumé

Safety concerns in the operation of autonomous aerial systems require safe-landing protocols be followed during situations where the mission should be aborted due to mechanical or other failure. This article presents a pulse-coupled neural network (PCNN) to assist in the vegetation classification in a vision-based landing site detection system for an unmanned aircraft. We propose a heterogeneous computing architecture and an OpenCL implementation of a PCNN feature generator. Its performance is compared across OpenCL kernels designed for CPU, GPU, and FPGA platforms. This comparison examines the compute times required for network convergence under a variety of images to determine the plausibility for real-time feature detection. References Altera. Implementing FPGA design with the OpenCL standard. White Paper, Altera Inc., November 2012. http://www.altera.com/literature/wp/wp-01173-opencl.pdf Bittware. S5-PCIe-HQ user guide. Technical report, Bittware, Inc., September 2013. M. T. DeGarmo. Issues concerning integration of unmanned aerial vehicles in civil airspace. Technical Report mP 04W0000323, MITRE Corporation, 2004. https://www.mitre.org/sites/default/files/pdf/04_1232.pdf R. Eckhorn, H. J. Reiboeck, M. Arndt, and P. W. Dicke. A neural network for feature linking via synchronous activity: Results from a cat visual cortex and from simulations. In Models of Brain Function, pages 255–272, Cambridge, UK, 1989. Cambridge University Press. Khronos OpenCL Working Group. The OpenCL specification. Technical report, Khronos Group, October 2009. http://www.khronos.org/registry/cl/specs/opencl-1.0.pdf X. Gu, Y. Fang, and Y. Wang. Attention selection using global topological properties based on pulse coupled neural network. Computer Vision and Image Understanding, 117:1400–1411, 2013. doi:10.1016/j.cviu.2013.05.004 X. Gu, L. Zhang, and D. Yu. General design approach to unit-linking pcnn for image processing. In Proceedings of International Joint Conference on Neural Networks, pages 1837–1841, 2005. doi:10.1109/IJCNN.2005.1556159 Z. Li, R. F. Hayward, R. A. Walker, and Y. Liu. A biologically inspired object spectral-texture descriptor and its application to vegetation classification in power-line corridors. IEEE Geoscience and Remote Sensing Letters, 8(4):631–635, 2011. doi:10.1109/LGRS.2010.2098391 A. Lu, W. Ding, J. Wang, and H. Li. Automonmous vision-based safe area selection algorithm for UAV emergency forced landing. In Proceedings of the International Conference on Information and Computer Applications 2012, pages 254–261, 2012. doi:10.1007/978-3-642-34041-3_37 L. Mejias and P. Eng. Controlled emergency landing of an unpowered unmanned aerial system. Journal of Intelligent and Robotic Systems, 70:421–435, 2013. doi:10.1007/s10846-012-9767-5 L. Mejias and D. L. Fitzgerald. A multi-layered approach for site detection in uas emergency landing scenarios using geometry-based image segmentation. In Proceedings of the 2013 International Conference on Unmanned Aerial Systems, pages 366–372, Atlanta, Georgia, 2013. IEEE Control Society. doi:10.1109/ICUAS.2013.6564710 L. Mejias, D. L. Fitzgerald, P. C. Eng, and L. Xi. Forced landing technologies for unmanned aerial vehicles : towards safer operations. In Aerial Vehicles, pages 415–442, Kirchengasse, Austria, 2009. In-Tech. doi:10.5772/6481 US-OSoD. Unmanned systems integrated roadmap fy2011-2036. Technical report, Office of the Secretary of Defense, US, 2011. http://info.publicintelligence.net/DoD-UAS-2011-2036.pdf Z. Wang, Y. Ma, F. Cheng, and L. Yang. Review of pulse-coupled neural networks. Image and Vision Computing, 28:5–13, 2010. doi:10.1016/j.imavis.2009.06.007

Détail

Publié le : 2014-01-01
DOI : https://doi.org/10.21914/anziamj.v55i0.7851