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.