Traffic is a machine learning project designed to classify traffic sign images using a convolutional neural network (CNN). The goal is to build a model capable of identifying various traffic signs with high accuracy, simulating real-world applications like autonomous vehicles.
This project focuses on image classification, specifically recognizing and categorizing traffic signs. By training a convolutional neural network (CNN), it processes image data to:
- Detect specific traffic signs (e.g., speed limits, warnings, etc.). 🛑
- Classify signs into predefined categories using supervised learning. 🧠
- Leverage computer vision for real-world applications. 🚗
- Designed for image processing tasks.
- Utilizes layers like convolution, pooling, and fully connected layers to extract and analyze features.
- Enables the model to recognize shapes, edges, and patterns in images.
- Frameworks used for building and training the CNN.
- Provides high-level APIs for model architecture design and optimization.
- Supports evaluation with metrics like accuracy and confusion matrices.
- Used for preprocessing the dataset and managing image data efficiently.
- Assists in image loading, resizing, and transformations.
The model is trained on traffic sign datasets, such as the German Traffic Sign Recognition Benchmark (GTSRB).
- Image Resizing: Images are resized to a fixed dimension (e.g., 30x30 pixels).
- Normalization: Pixel values are normalized to improve model convergence.
- Label Encoding: Signs are categorized into numerical labels for supervised learning.
- Convolutional Layers: Extract features like edges and shapes.
- Pooling Layers: Reduce dimensionality while retaining essential features.
- Fully Connected Layers: Perform classification based on extracted features.
- Softmax Output: Produces probabilities for each class, identifying the most likely traffic sign.
- Supervised Learning: The CNN is trained using labeled traffic sign images.
- Loss Function: Categorical cross-entropy measures the error between predicted and actual labels.
- Optimizer: Adam optimizer adjusts weights to minimize loss.
- The model is evaluated using metrics like accuracy and confusion matrices to measure performance on unseen data.
- Accuracy: Achieves over 95% accuracy on benchmark datasets after training.
- Robustness: Handles variations in lighting, orientation, and scale due to data augmentation techniques.
- Generalization: Performs well on unseen images, showcasing its potential for real-world applications.
This project demonstrates the use of AI and computer vision in:
- Autonomous Vehicles: Recognizing traffic signs for safe navigation.
- Traffic Monitoring: Classifying signs from roadside images or video feeds.
- Driver Assistance Systems: Enhancing safety by alerting drivers to detected signs.
- Hands-On Learning: Understand and apply CNNs to a practical image classification problem.
- Real-World Impact: Explore AI's role in transportation and safety.
- Scalable: The approach can be extended to other computer vision tasks, like pedestrian detection or vehicle recognition.
If you have questions, suggestions, or ideas for extending this project, feel free to reach out! 🚀