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https://github.com/hamidhosen42/pothole-detection-using-transfer-learning-models-a-comparative-study
Pothole Detection Using Transfer Learning Models: A Comparative Study
https://github.com/hamidhosen42/pothole-detection-using-transfer-learning-models-a-comparative-study
cnn deep-learning detection inceptionresnetv2 inceptionv3 mobilenetv2 plain pothole-detection vgg16 vgg19 xception
Last synced: 11 days ago
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Pothole Detection Using Transfer Learning Models: A Comparative Study
- Host: GitHub
- URL: https://github.com/hamidhosen42/pothole-detection-using-transfer-learning-models-a-comparative-study
- Owner: hamidhosen42
- Created: 2023-12-10T05:50:10.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2024-01-06T17:17:16.000Z (11 months ago)
- Last Synced: 2024-04-08T09:04:25.837Z (8 months ago)
- Topics: cnn, deep-learning, detection, inceptionresnetv2, inceptionv3, mobilenetv2, plain, pothole-detection, vgg16, vgg19, xception
- Language: Jupyter Notebook
- Homepage:
- Size: 41.2 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Pothole Detection Using Transfer Learning Models: A Comparative Study
In this fast-paced modern world potholes
are considered as some random holes on the surface of
the roads and are considered as mere obstacles while
traveling. But reality is much harsher than these
considerations as these mere potholes are solely
responsible for a significant amount of road accidents
which involve hundreds of deaths and much higher
property damages. This is a detailed comparative study
of some popular deep-learning algorithms. The main
objective of this comparative study is to find a better
solution to tackle the pothole problem faced by the
countries whose economy is based mostly on transport
systems. The base model of these algorithms is tweaked
to bring out their best results on the used dataset.
Results are decided based on the output accuracy
delivered by the respective algorithms. These algorithms
include CNN, VGG19, VGG16, InceptionResNetV2,
InceptionV3, MobileNetV2, and Xception. The
MobileNetV2 with layer freezing has emerged as the best
of all models used in this study with an accuracy of
96.37%. It has also taken the least computational time
for each image.
The dataset that was used for this research was
taken from Kaggle as it is the largest worldwide data science
community, providing powerful tools and useful resources
to help you achieve your data science goals. The data used
contains images of various conditions of roads, including
rainy environments, waterlogged potholes, camouflaged
potholes, and many more, which satisfies a future work
from the literature review that needed detection of potholes
in extreme conditions. The potholes were also captured from
different ranges, from a very close range to a far range
which will make the model more versatile for detection.
Another condition of taking pictures from the dashboard
camera of the cars was also satisfied as the used dataset
contains images taken from inside the cars. The data type of
all images is in JPEG format as it has reduced file size,
faster data loading, less memory usage, and less bandwidth
usage. The dataset is divided into 2 parts: train part and test
part, containing a total of 6096 images. The training part is
also further divided into 2 parts one contains pothole images
another contains plain road images; this is the same with the
testing part. The data is divided by 80% to the training and
20% to the testing, allocating 5075 images in training and
1021 images in testing. Training contains 2508 plain road
images and 2567 pothole images, on the other hand in
testing there are 509 plain images and 512 pothole images.
A pothole is generally a hole formed on a road by
erosion. Depending on the extent of the damage, their sizes
vary from small to large. Their increased sizes also increase
the damage it does. Early detection can decrease the amount
of these damages. To address this problem, various
algorithms have been employed, including CNN, VGG19,
VGG16, MobileNetV2, InceptionResNetV2, InceptionV3,
MobileNetV2, and Xception. Among these models,
MobileNetV2 emerges as the best performer, achieving a
96.37% accuracy rate. It also has the best precision, recall,
f1-score, and computational time acquiring 96.44%,
96.38%, 96.37%, and 193ms respectively. Considering the
accuracy achieved, overall performance, and the
computation time it takes for each step, MobileNetV2 is the
best choice for Pothole detection.
## RESULTS
## Authors
- [@hamidhosen42](https://www.github.com/hamidhosen42)
## š Skills
CNN, VGG16, VGG19, MobileNet-v2, Inception-V3, Xception, Inception, ResNetV2
## š About Me
š Iām currently working on Flutter App Developer and Machine Learning
š± Iām currently learning Deep Learning and NLP
šÆ Iām looking to collaborate on Flutter and ReactJs and Machine Learning
š« How to reach me [email protected]
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## Badges
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