  Poor roads are a major issue for cars, drivers, and pedestrians since they
are a major cause of vehicle damage and can occasionally be quite dangerous for
both groups of people (pedestrians and drivers), this makes road surface
condition monitoring systems essential for traffic safety, reducing accident
rates ad also protecting vehicles from getting damaged. The primary objective
is to develop and evaluate machine learning models that can accurately classify
road conditions into four categories: good, satisfactory, poor, and very poor,
using a Kaggle dataset of road images. To address this, we implemented a
variety of machine learning approaches. Firstly, a baseline model was created
using a Multilayer Perceptron (MLP) implemented from scratch. Secondly, a more
sophisticated Deep Neural Network (DNN) was constructed using Keras.
Additionally, we developed a Logistic Regression model from scratch to compare
performance. Finally, a wide model incorporating extensive feature engineering
was built using the K-Nearest Neighbors (KNN) algorithm with sklearn.The study
compared different models for image-based road quality assessment. Deep
learning models, the DNN with Keras achieved the best accuracy, while the
baseline MLP provided a solid foundation. The Logistic Regression although it
is simpler, but it provided interpretability and insights into important
features. The KNN model, with the help of feature engineering, achieved the
best results. The research shows that machine learning can automate road
condition monitoring, saving time and money on maintenance. The next step is to
improve these models and test them in real cities, which will make our cities
better managed and safer.
