The Role Of Deep Learning In Traffic Sign Recognition

Traffic Sign Recognition (TSR) is an essential component in automated vehicle guidance systems. Modern systems leverage deep learning models to recognize and classify traffic signs accurately. In this blog post, we explore the application of Convolutional Neural Networks (CNN) in this crucial field of Data Science.

What Is Traffic Sign Recognition?

Traffic Sign Recognition is a technology incorporated in advanced driver-assistance systems (ADAS) to identify and read traffic signs. TSR systems are trained to recognize the shapes, colors, and detailed contents of the signs.

CNN for Traffic Sign Recognition

CNN is a type of deep neural network that effectively processes images, which makes it the choicest model for TSR. It does pixel multiplication for images and returns values consistent with the corresponding traffic sign.

Working with Python and Keras

For this exercise, we will use Python and its powerful libraries: Keras for building the CNN model and OpenCV for image processing. We'll focus on the German Traffic Signs Recognition Benchmark (GTSRB), a multi-class, single-image classification challenge.

import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt 
import tensorflow as tf 
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout
from tensorflow.keras.optimizers import Adam
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from sklearn.preprocessing import LabelBinarizer

To load and preprocess the dataset:

train_data = pd.read_csv('/path/to/Train.csv')
labels = LabelBinarizer().fit_transform(train_data['ClassId'])
x_train, x_val, y_train, y_val = train_test_split(train_data, labels, test_size=0.3, random_state=42)
x_train = np.array(list(map(lambda x: cv2.resize(cv2.imread('/path/to/train/'+str(x)), (64,64)), x_train['Path'].values)))
x_val = np.array(list(map(lambda x: cv2.resize(cv2.imread('/path/to/train/'+str(x)), (64,64)), x_val['Path'].values)))
x_train = x_train / 255.0
x_val = x_val / 255.0

And finally to build, compile and train the CNN model:

model = Sequential()
model.add(Conv2D(filters=32, kernel_size=(5,5), activation='relu', input_shape=x_train.shape[1:]))
model.add(Conv2D(filters=64, kernel_size=(3,3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(rate=0.25))
model.add(Conv2D(filters=64, kernel_size=(3,3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(rate=0.25))
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(rate=0.5))
model.add(Dense(43, activation='softmax'))

model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.0001), metrics=['accuracy'])
history = model.fit(x_train, y_train, batch_size=32, epochs=20, validation_data=(x_val, y_val))

In conclusion, Traffic Sign Recognition is a key application of deep learning that is bound to increase the safety and efficiency of our transport systems.

In this blog post, we have merely scratched the surface. There is much more to explore such as improving the model’s performance and refining the preprocessing steps for better results.