Pothole Detection using CNNs 🚗

Convolutional Neural Network (CNN) implementation for detecting potholes in images. 📸

Table of Contents

• Overview
• Setup
• Usage
• Code Structure

Overview

This project aims to detect potholes in images using a CNN-based approach. The code provided here is structured to preprocess the data, build a CNN model using TensorFlow, train the model, and make predictions on test images.

Setup

Prerequisites

• Python 3.x
• TensorFlow
• OpenCV
• NumPy
• Matplotlib

Installation

pip install opencv-python
pip install tensorflow
# Add any additional libraries as needed

Prerequisites

1. Clone the repository.
2. Open the project in your Python environment.
3. Run the provided code snippets in a compatible Python environment.

Code Structure

This repository contains code that covers the following steps:

1. Importing Libraries : Initializing and importing necessary libraries and configurations.
2. Data Verification : Confirming dataset availability by displaying sample images.
3. Data Preprocessing : Preparing the dataset for training and validation.
4. Building the CNN : Creating the CNN model architecture using TensorFlow's Sequential API.
5. Training the CNN : Compiling and training the CNN model on the prepared dataset.
6. Making Predictions : Utilizing the trained model to predict potholes in test images.