Stagnant zone segmentation with U-net

Selam Waktola, Laurent Babout, Krzysztof Grudzien

Abstract:

Silo discharging and monitoring the process for
industrial or research application depend on computerized
segmentation of different parts of images such as stagnant and
flowing zones which is the toughest task. X-ray Computed
Tomography (CT) is one of a powerful non-destructive technique
for cross-sectional images of a 3D object based on X-ray
absorption. CT is the most proficient for investigating different
granular flow phenomena and segmentation of the stagnant zone
as compared to other imaging techniques. In any case, manual
segmentation is tiresome and erroneous for further investigations.
Hence, automatic and precise strategies are required. In the
present work, a U-net architecture is used for segmenting the
stagnant zone during silo discharging process. This proposed
image segmentation method provides fast and effective outcomes
by exploiting a convolutional neural networks technique with an
accuracy of 97 percent