Authors :
Aditya Prakash Devrukhkar; Aditya Anand Dethe; Sugamkumar Patel; Swapnil Fulkant Londhe
Volume/Issue :
Volume 7 - 2022, Issue 4 - April
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3MsLsoa
DOI :
https://doi.org/10.5281/zenodo.6496700
Abstract :
Object Detection Potholes are a traffic
hazard, endangering the safety of both automobiles
and pedestrians. It is one of the leading causes of road
accidents and the loss of lives and property in most
developing countries. As a response, there is a need to
collect and update data on current road conditions on
a regular basis so that vehicles may be warned of
other routes and the appropriate government
department can take urgent action to remove potholes
for the benefit of commuters. Using object
identification algorithms on photos captured with a
smartphone camera is a simple and effective
technique to locate potholes on roadways. As a result,
the goal of this research is to evaluate the
performance of state-of-the-art neural network
algorithms such as YOLO and Faster R-CNN with
VGG16 and ResNet-18 architectures for rapid and
accurate pothole identification. Furthermore, an
updated YOLOv2 architecture is suggested to address
the "pothole" and "regular road" class imbalance
problem, and its performance is compared to that of
existing object recognition algorithms utilising
accuracy, recall, intersection over union, and frames
processed per second (FPS). For real-time geotagged
pothole recognition from images, this model can be
used in autonomous cars. Pothole detecting software
may also offer alternative environmentally friendly
routes and assist commuters with low-light
navigation.
Keywords :
Autonomous Vehicle; Deep Learning Neural Network; CNN; YOLO Algoritham Object Detection; Image Processing .
Object Detection Potholes are a traffic
hazard, endangering the safety of both automobiles
and pedestrians. It is one of the leading causes of road
accidents and the loss of lives and property in most
developing countries. As a response, there is a need to
collect and update data on current road conditions on
a regular basis so that vehicles may be warned of
other routes and the appropriate government
department can take urgent action to remove potholes
for the benefit of commuters. Using object
identification algorithms on photos captured with a
smartphone camera is a simple and effective
technique to locate potholes on roadways. As a result,
the goal of this research is to evaluate the
performance of state-of-the-art neural network
algorithms such as YOLO and Faster R-CNN with
VGG16 and ResNet-18 architectures for rapid and
accurate pothole identification. Furthermore, an
updated YOLOv2 architecture is suggested to address
the "pothole" and "regular road" class imbalance
problem, and its performance is compared to that of
existing object recognition algorithms utilising
accuracy, recall, intersection over union, and frames
processed per second (FPS). For real-time geotagged
pothole recognition from images, this model can be
used in autonomous cars. Pothole detecting software
may also offer alternative environmentally friendly
routes and assist commuters with low-light
navigation.
Keywords :
Autonomous Vehicle; Deep Learning Neural Network; CNN; YOLO Algoritham Object Detection; Image Processing .
