Journal article

Accelerating computer-based recognition of fynbos leaves using a Graphics Processing Unit


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Publication Details

Author list: Winberg SL, Naidoo K, Ramone M

Publisher: SA Institute of Computer Scientists and Information Technologists; c/o The Head of Department; Floor8; Theo van Wyk; Room42; School of Computing; Pretoria Tel:012 429 6817 E-mail:kotzep@unisa.ac.za;

Publication year: 2017

Journal: South African Computer Journal

Volume number: 29

Issue number: 3

Start page: 238

End page: 262

Total number of pages: 25

ISSN: 1015-7999

URL: http://sacj.cs.uct.ac.za/index.php/sacj/article/view/432


Abstract



The Cape Floristic Kingdom (CFK)
is the most diverse floristic kingdom in the world and has been declared an
international heritage site. However, it is under threat from wild fires and
invasive species. Much of the work of managing this natural resource, such as
removing alien vegetation or fighting wild fires, is done by volunteers and
casual workers. The Fynbos Leaf Optical Recognition Application (FLORA) was
developed to assist in the recognition of plants of the CFK. The first version
of FLORA was developed as a rapid prototype in MATLAB, but suffered from slow
performance and did not run as a lightweight standalone executable. FLORA was
thus re-developed as a standalone C++ application and
subsequently enhanced using a graphics processing unit (GPU). This paper
presents all three versions, viz., the MATLAB prototype, the C++ non-accelerated
version, and the C++ GPU-accelerated version. The accuracy of
predictions remained consistent. The C++ version was
noticeable faster than the original prototype, achieving an average speed-up of
42 for high-resolution images. The GPU-accelerated version was even faster
achieving an average speed-up of 54. Such time saving would be perceptible for
batch processing, such as rebuilding feature descriptors in the leaf database.




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Last updated on 2018-07-02 at 11:38