  Pavement condition evaluation is essential to time the preventative or
rehabilitative actions and control distress propagation. Failing to conduct
timely evaluations can lead to severe structural and financial loss of the
infrastructure and complete reconstructions. Automated computer-aided surveying
measures can provide a database of road damage patterns and their locations.
This database can be utilized for timely road repairs to gain the minimum cost
of maintenance and the asphalt's maximum durability. This paper introduces a
deep learning-based surveying scheme to analyze the image-based distress data
in real-time. A database consisting of a diverse population of crack distress
types such as longitudinal, transverse, and alligator cracks, photographed
using mobile-device is used. Then, a family of efficient and scalable models
that are tuned for pavement crack detection is trained, and various
augmentation policies are explored. Proposed models, resulted in F1-scores,
ranging from 52% to 56%, and average inference time from 178-10 images per
second. Finally, the performance of the object detectors are examined, and
error analysis is reported against various images. The source code is available
at https://github.com/mahdi65/roadDamageDetection2020.
