Authors :
Audu-war, Samuel Ochai; Anigbogu Sylvanus Okwudili; Anigbogu Kenechukwu Sylvanus; Anigbogu Gloria Nkiru; Asogwa Doris Chinedu
Volume/Issue :
Volume 8 - 2023, Issue 7 - July
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/mtr6zsdy
DOI :
https://doi.org/10.5281/zenodo.8262321
Abstract :
Potholes are one the major concerns of
underdeveloped and developed nations. Roads are the
essential means of transportation for a country and when
this road becomes bad safe driving is threatened, this may
result to road traffic crashes hence the need to provide an
intelligence system that can detect this potholes in real
time and give drivers real time feedback to enable them
make adequate decisions while driving. This paper
presented the pothole detection model that was trained
with data extracted from Google and some real time data.
Region Convolution Neutral Network (R-CNN) as an
object detection model was used to analyze images
captured via cameras used for image detection
specifically for Pothole Detection, and only part of the
image is processed instead of the background; hence very
large data and consequently tedious computations, pixel
matching, parameter updating and sorting were
significantly decreased. This work used the comparative
analysis, Microsoft Common Object in Context (COCO)
and TFLITE mobile net. The model was evaluated and
their strengths and limitations were analyzed based on
metric parameters such as accuracy, precision and F1
score. The results analyzed show that the suitability of the
algorithms over is depended to a great extent to the use
cases they were applied in. In a good testing environment,
Region Convolution Neural Network (R-CNN) gave a
good classification report with the parallel testing proof
that the model is not perverse.
Keywords :
Machine Learning, Object detection, Pothole detection, Region Convolution Neural Network.
Potholes are one the major concerns of
underdeveloped and developed nations. Roads are the
essential means of transportation for a country and when
this road becomes bad safe driving is threatened, this may
result to road traffic crashes hence the need to provide an
intelligence system that can detect this potholes in real
time and give drivers real time feedback to enable them
make adequate decisions while driving. This paper
presented the pothole detection model that was trained
with data extracted from Google and some real time data.
Region Convolution Neutral Network (R-CNN) as an
object detection model was used to analyze images
captured via cameras used for image detection
specifically for Pothole Detection, and only part of the
image is processed instead of the background; hence very
large data and consequently tedious computations, pixel
matching, parameter updating and sorting were
significantly decreased. This work used the comparative
analysis, Microsoft Common Object in Context (COCO)
and TFLITE mobile net. The model was evaluated and
their strengths and limitations were analyzed based on
metric parameters such as accuracy, precision and F1
score. The results analyzed show that the suitability of the
algorithms over is depended to a great extent to the use
cases they were applied in. In a good testing environment,
Region Convolution Neural Network (R-CNN) gave a
good classification report with the parallel testing proof
that the model is not perverse.
Keywords :
Machine Learning, Object detection, Pothole detection, Region Convolution Neural Network.
