Pavement Distress Detection Using Deep Learning Based Methods: A Survey on Role, Challenges and Opportunities

Ankit Khatri, Ravi Khatri

Abstract:

Roadways have always been one of the most used modes of transportation, and their contribution to the nation’s economy is also huge. To meet the demands of the growing global population and an increase in urbanization, there
has been an exponential rise in the number of vehicles plying on the roads as well
as the length of the roads. With this increase in traffic, coupled with other issues
like heavy rainfall, the material used for the construction of the road, etc., the
condition of the roads deteriorates with cracks and potholes developing on them,
which may lead to serious accidents. For effective maintenance of roads and to
reduce the associated risks, these defects must be detected. With the advent of
Deep Learning (DL) in the recent past and its applications in various sectors, we
have comprehensively explored various approaches, particularly using DL in this
study, along with the associated challenges in adopting such techniques and future
opportunities in this domain. Based on our analysis, using object detection-based
models turned out to outperform other approaches.