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Proceedings Paper

Applications of a morphological scene change detection (MSCD) for visual leak and failure identification in process and chemical engineering
Author(s): Andrew J. Tickle; Paul K. Harvey; Jeremy S. Smith
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Paper Abstract

Morphological Scene Change Detection (MSCD) is a process typically tasked at detecting relevant changes in a guarded environment for security applications. This can be implemented on a Field Programmable Gate Array (FPGA) by a combination of binary differences based around exclusive-OR (XOR) gates, mathematical morphology and a crucial threshold setting. The additional ability to set up the system in virtually any location due to the FPGA makes it ideal for insertion into an autonomous mobile robot for patrol duties. However, security is not the only potential of this robust algorithm. This paper details how such a system can be used for the detection of leaks in piping for use in the process and chemical industries and could be deployed as stated in the above manner. The test substance in this work was water, which was pumped either as a liquid or as low pressure steam through a simple pipe configuration with holes at set points to simulate the leaks. These holes were situated randomly at either the center of a pipe (in order to simulate an impact to it) or at a joint or corner (to simulate a failed weld). Imagery of the resultant leaks, which were visualised as drips or the accumulation of steam, which where analysed using MATLAB to determine their pixel volume in order to calibrate the trigger for the MSCD. The triggering mechanism is adaptive to make it possible in theory for the type of leak to be determined by the number of pixels in the threshold of the image and a numerical output signal to state which of the leak situations is being observed. The system was designed using the DSP Builder package from Altera so that its graphical nature is easily comprehensible to the non-embedded system designer. Furthermore, all the data from the DSP Builder simulation underwent verification against MATLAB comparisons using the image processing toolbox in order to validate the results.

Paper Details

Date Published: 3 November 2010
PDF: 8 pages
Proc. SPIE 7833, Unmanned/Unattended Sensors and Sensor Networks VII, 78330W (3 November 2010); doi: 10.1117/12.864994
Show Author Affiliations
Andrew J. Tickle, Univ. of Liverpool (United Kingdom)
Paul K. Harvey, Univ. of Liverpool (United Kingdom)
Jeremy S. Smith, Univ. of Liverpool (United Kingdom)


Published in SPIE Proceedings Vol. 7833:
Unmanned/Unattended Sensors and Sensor Networks VII
Edward M. Carapezza, Editor(s)

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