Abstract:
Road damages are a big issue for a developing country like Bangladesh. Manually
maintained road damages are costly and time-consuming. So, it’s a need of time to make
a system that will automatically classify the road damage to let the authority understand
which roads are mode damaged and which one is less. It’s also needed for drivers to safely
drive a car on the road. Developed countries already made a system that is not affordable
for Bangladesh. So, I have decided to make a system that will help the authority to classify
road damages at a low cost. So, I have used transfer learning of convolutional neural
network which will help to make a system to classify road damages at low cost, because
transfer learning is a system where we can reuse the code. There I have used 5 models of
convolutional neural networks and all of them were transfer learning methods. They are
Xception, InceptionV3, VGG16, VGG19, and DenseNet201. All the model’s model
accuracy, model loss, confusion matrix, and classification results have been generated. But
among all of the five models, VGG16’s gives the highest accuracy score of 92%. In the
future, I will work on detecting road damage and will increase the dataset to get higher
accuracy and with other classification types of roads damages.
