312 Prakt. Met. Sonderband 52 (2018)
Table 1: Model comparison gildana
noe!
Large U-Net Small U-Net Small U-Net (scaled) 0
Iterations 326000(~1 week) 300000(~5 days) 65000(~1 day) =
#Parameters > 33 million > 15 million > 5 million
mioU 0.83399 0.81243 0.78289
Deviation 94.4% 96.9% 96.7%
Evaluationtime ~~ 23s/img ~ 9sfimg ~ 0.4s/img
. . (urzfass
6. Summary and Discussion
Deep Learning is used to segment metallographic images fully automatically in very good pe
accordance to ground truth images segmented in a semiautomatic workflow. To achieve a Sr m
uniform image input we investigated suitable pre-processing techniques. Categorical Tan
encoding of the jpeg compressed image by thresholding and contrasting by Contrast we
Limited Adaptive Histogram Equalization shows good performance. We demonstrate a
tiling strategy to partition input images larger than the U-Net input pixel-size, that contains
neighbor and border information. We evaluated three Fully Convolutional Network (FCN) jEinlen
architectures based on U-Net. The “Large U-Net” results in the highest mloU but has not
the highest conformance to the acceptable range. For the used data set it turned out, that vee
the trainable depth of the FCN can be reduced which leads to an increase in speed without Avon
losing quality. For an even faster FCN, the used overlap-tile strategy could also be kn einher
replaced by scaling down without significant change in the determined phase fractions. pine
The solved image analysis task is relative complex. Thus we are confident, that the “U- x entsta
Net” can also be used for other completely different image analysis tasks in the future. rr
set und
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of
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