  The Data Science for Pavement Challenge (DSPC) seeks to accelerate the
research and development of automated vision systems for pavement condition
monitoring and evaluation by providing a platform with benchmarked datasets and
codes for teams to innovate and develop machine learning algorithms that are
practice-ready for use by industry. The first edition of the competition
attracted 22 teams from 8 countries. Participants were required to
automatically detect and classify different types of pavement distresses
present in images captured from multiple sources, and under different
conditions. The competition was data-centric: teams were tasked to increase the
accuracy of a predefined model architecture by utilizing various data
modification methods such as cleaning, labeling and augmentation. A real-time,
online evaluation system was developed to rank teams based on the F1 score.
Leaderboard results showed the promise and challenges of machine for advancing
automation in pavement monitoring and evaluation. This paper summarizes the
solutions from the top 5 teams. These teams proposed innovations in the areas
of data cleaning, annotation, augmentation, and detection parameter tuning. The
F1 score for the top-ranked team was approximately 0.9. The paper concludes
with a review of different experiments that worked well for the current
challenge and those that did not yield any significant improvement in model
accuracy.
