🚦 Traffic Sign Detection

Deep Learning Project using Convolutional Neural Networks (CNNs)

🚀 I'm thrilled to share my latest project on Traffic Sign Detection, where I implemented deep learning techniques to detect and classify traffic signs. The project was developed on Kaggle and explores how CNNs can be utilized to accurately recognize various traffic signs in real-world environments.

🔍 Project Overview:

The objective of this project is to build a model capable of recognizing different types of traffic signs, which is crucial for autonomous driving systems and advanced driver-assistance systems (ADAS).

🛠 Tools & Techniques:

• Python 🐍
• Keras: For building the deep learning model.
• TensorFlow: As the backend framework.
• OpenCV: For image preprocessing and augmentation.

🧠 Modeling:

I developed a Convolutional Neural Network (CNN) to classify traffic signs. The model was trained on a dataset of labeled traffic signs and achieved promising results, thanks to advanced data augmentation techniques.

🔧 Model Performance:

After fine-tuning the hyperparameters and augmenting the data, the model achieved high accuracy in detecting traffic signs, contributing to the safe and reliable navigation of autonomous systems.

📊 Visualizations:

• Confusion matrix to evaluate the model’s performance.
• Accuracy and loss graphs to track the training process.

NoteBook on Kaggle: https://www.kaggle.com/code/ahmedelsany/traffic-sign-detection

Dataset : https://www.kaggle.com/datasets/meowmeowmeowmeowmeow/gtsrb-german-traffic-sign

This project is part of my continuous learning journey in the world of deep learning and computer vision. Looking forward to exploring more innovative applications in the future!