Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning Frameworks for Pavement Distress Classification: A
  Comparative Analysis   Automatic detection and classification of pavement distresses is critical in
timely maintaining and rehabilitating pavement surfaces. With the evolution of
deep learning and high performance computing, the feasibility of vision-based
pavement defect assessments has significantly improved. In this study, the
authors deploy state-of-the-art deep learning algorithms based on different
network backbones to detect and characterize pavement distresses. The influence
of different backbone models such as CSPDarknet53, Hourglass-104 and
EfficientNet were studied to evaluate their classification performance. The
models were trained using 21,041 images captured across urban and rural streets
of Japan, Czech Republic and India. Finally, the models were assessed based on
their ability to predict and classify distresses, and tested using F1 score
obtained from the statistical precision and recall values. The best performing
model achieved an F1 score of 0.58 and 0.57 on two test datasets released by
the IEEE Global Road Damage Detection Challenge. The source code including the
trained models are made available at [1].

5
The categories of this paper are: gr-qc
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The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: eess
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The categories of this paper are: q-bio
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The categories of this paper are: nucl-th
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The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
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The categories of this paper are: acc-phys
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The categories of this paper are: astro-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-th
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The categories of this paper are: math