Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Impact of Ground Truth Annotation Quality on Performance of Semantic
  Image Segmentation of Traffic Conditions   Preparation of high-quality datasets for the urban scene understanding is a
labor-intensive task, especially, for datasets designed for the autonomous
driving applications. The application of the coarse ground truth (GT)
annotations of these datasets without detriment to the accuracy of semantic
image segmentation (by the mean intersection over union - mIoU) could simplify
and speedup the dataset preparation and model fine tuning before its practical
application. Here the results of the comparative analysis for semantic
segmentation accuracy obtained by PSPNet deep learning architecture are
presented for fine and coarse annotated images from Cityscapes dataset. Two
scenarios were investigated: scenario 1 - the fine GT images for training and
prediction, and scenario 2 - the fine GT images for training and the coarse GT
images for prediction. The obtained results demonstrated that for the most
important classes the mean accuracy values of semantic image segmentation for
coarse GT annotations are higher than for the fine GT ones, and the standard
deviation values are vice versa. It means that for some applications some
unimportant classes can be excluded and the model can be tuned further for some
classes and specific regions on the coarse GT dataset without loss of the
accuracy even. Moreover, this opens the perspectives to use deep neural
networks for the preparation of such coarse GT datasets.

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The categories of this paper are: dg-ga
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The categories of this paper are: acc-phys
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The categories of this paper are: bayes-an
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The categories of this paper are: econ
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The categories of this paper are: adap-org
3
The categories of this paper are: physics
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The categories of this paper are: atom-ph
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The categories of this paper are: math-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: patt-sol
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The categories of this paper are: ao-sci
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The categories of this paper are: cond-mat
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The categories of this paper are: quant-ph
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The categories of this paper are: nlin
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The categories of this paper are: supr-con
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The categories of this paper are: cmp-lg