Authors :
Pranav Prashant Kulkarni; Ketan Mandar Kulkarni; Vivek Rajkumar Nimbalkar; S. P. Jadhav
Volume/Issue :
Volume 8 - 2023, Issue 2 - February
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://bit.ly/3moTvuz
DOI :
https://doi.org/10.5281/zenodo.7696183
Abstract :
In recent years, technology has contributed to
improving transportation systems, but the maintenance
and upkeep of road networks remain a challenge despite
these advancements. Potholes, cracks, and other road
defects can lead to accidents, traffic congestion, and
expensive repairs. An android pothole detection system
that utilizes smartphone sensors and machine learning
algorithms has the potential to revolutionize road
maintenance and safety. This proposed system will use a
smartphone camera to detect potholes and distinguish
them from other road irregularities. It will also be
integrated with a backend database that can store and
analyze data on road conditions, enabling authorities to
prioritize maintenance and repairs. The android pothole
detection system can offer several benefits, including
reducing road accidents, lowering repair costs, and
minimizing traffic congestion. Our proposed solution aims
to crowdsource information from people who face these
issues and forward it to relevant authorities using an
Android application. To achieve this, we will utilize a Deep
Learning model capable of detecting potholes, collecting
information from users, and sending it to authorities. The
success of this solution depends on the accuracy of the
Deep Learning model, the quality of user-provided
information, the responsiveness of relevant authorities,
and user engagement. Therefore, the system must have
appropriate parameters to manage these factors and
guarantee the solution's effectiveness. In conclusion,
utilizing technology for android pothole detection can lead
to effective and timely repairs, contributing to overall road
safety and reducing vehicle damage. The proposed android
pothole detection system brings people together to work on
a common problem and has the potential to revolutionize
road maintenance and safety, providing safer and more
efficient means of travel for everyone.
Keywords :
Deep Learning , Road Safety , Efficient Means To Travel , Smartphone Sensors.
In recent years, technology has contributed to
improving transportation systems, but the maintenance
and upkeep of road networks remain a challenge despite
these advancements. Potholes, cracks, and other road
defects can lead to accidents, traffic congestion, and
expensive repairs. An android pothole detection system
that utilizes smartphone sensors and machine learning
algorithms has the potential to revolutionize road
maintenance and safety. This proposed system will use a
smartphone camera to detect potholes and distinguish
them from other road irregularities. It will also be
integrated with a backend database that can store and
analyze data on road conditions, enabling authorities to
prioritize maintenance and repairs. The android pothole
detection system can offer several benefits, including
reducing road accidents, lowering repair costs, and
minimizing traffic congestion. Our proposed solution aims
to crowdsource information from people who face these
issues and forward it to relevant authorities using an
Android application. To achieve this, we will utilize a Deep
Learning model capable of detecting potholes, collecting
information from users, and sending it to authorities. The
success of this solution depends on the accuracy of the
Deep Learning model, the quality of user-provided
information, the responsiveness of relevant authorities,
and user engagement. Therefore, the system must have
appropriate parameters to manage these factors and
guarantee the solution's effectiveness. In conclusion,
utilizing technology for android pothole detection can lead
to effective and timely repairs, contributing to overall road
safety and reducing vehicle damage. The proposed android
pothole detection system brings people together to work on
a common problem and has the potential to revolutionize
road maintenance and safety, providing safer and more
efficient means of travel for everyone.
Keywords :
Deep Learning , Road Safety , Efficient Means To Travel , Smartphone Sensors.
