Road Sign Recognition System

 Road Sign Recognition System – IT and Computer Engineering Guide

1. Project Overview

Objective: Develop a system that recognizes and classifies road signs using images, enabling its use in autonomous vehicles and driver-assistance systems.
Scope: Train a convolutional neural network (CNN) to accurately classify various types of road signs.

2. Prerequisites

Knowledge: Understanding of Python programming, convolutional neural networks (CNNs), and computer vision.
Tools: Python, TensorFlow/Keras, OpenCV, and NumPy.
Dataset: Publicly available datasets like the German Traffic Sign Recognition Benchmark (GTSRB) or custom data.

3. Project Workflow

- Dataset Collection: Gather labeled images of road signs from public or custom datasets.

- Data Preprocessing: Normalize, resize, and augment images to improve generalization.

- Model Development: Build and train a CNN for road sign classification.

- Evaluation: Assess the model's performance on unseen test data.

- Deployment: Integrate the model into real-world applications, such as autonomous vehicles.

4. Technical Implementation

Step 1: Import Libraries


import cv2
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
from tensorflow.keras.preprocessing.image import ImageDataGenerator

Step 2: Load and Preprocess Data


# Load dataset
data_dir = 'dataset/'
categories = ['Stop', 'Yield', 'Speed_Limit', 'No_Entry']  # Example categories

data = []
for category in categories:
    path = os.path.join(data_dir, category)
    class_num = categories.index(category)
    for img in os.listdir(path):
        try:
            img_array = cv2.imread(os.path.join(path, img), cv2.IMREAD_COLOR)
            resized_img = cv2.resize(img_array, (64, 64))
            data.append([resized_img, class_num])
        except Exception as e:
            pass

# Prepare data
X, y = zip(*data)
X = np.array(X) / 255.0
y = np.array(y)

Step 3: Train the Model


# Build CNN model
model = Sequential([
    Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)),
    MaxPooling2D(2, 2),
    Conv2D(64, (3, 3), activation='relu'),
    MaxPooling2D(2, 2),
    Flatten(),
    Dense(128, activation='relu'),
    Dropout(0.5),
    Dense(len(categories), activation='softmax')
])

# Compile model
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# Train model
model.fit(X, y, epochs=15, validation_split=0.2, batch_size=32)

Step 4: Evaluate and Save the Model


# Evaluate model
loss, accuracy = model.evaluate(X, y)
print(f"Model Accuracy: {accuracy}")

# Save model
model.save('road_sign_recognition_model.h5')

Step 5: Real-Time Recognition


# Load the trained model
model.load_weights('road_sign_recognition_model.h5')

# Initialize webcam or video feed
cap = cv2.VideoCapture(0)
while True:
    ret, frame = cap.read()
    resized_frame = cv2.resize(frame, (64, 64)).reshape(1, 64, 64, 3) / 255.0
    prediction = model.predict(resized_frame)
    label = categories[np.argmax(prediction)]
    cv2.putText(frame, label, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)
    cv2.imshow("Road Sign Recognition", frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

5. Results and Insights

Analyze the model's performance, including accuracy, precision, recall, and any misclassifications.

6. Challenges and Mitigation

Data Imbalance: Use data augmentation to balance the dataset.
Real-Time Processing: Optimize the model to work efficiently on edge devices.

7. Future Enhancements

Extend to multi-language recognition for road signs.
Integrate with GPS systems to provide context-aware alerts.

8. Conclusion

The Road Sign Recognition System project exemplifies the practical use of AI in enhancing transportation safety and enabling autonomous vehicles.