Traffic Signs Classification

Objective and Problem Statement

• Traffic Signs Classification is an important task for self driving cars. In this Project, I have prepared a Deep Convolutional Neural Network Model which can classify the images of 43 distinct types of Traffic Signals. It is a multi-class Classification Project. You can gain insights about the Impelementation of Deep Convolutional Neural Network in Image Classification.

Convolutional Neural Network

• In deep learning, a convolutional neural network is a class of deep neural networks, most commonly applied to analyzing visual imagery. They are also known as shift invariant or space invariant artificial neural networks, based on their shared-weights architecture and translation invariance characteristics.

Getting the Data

• I have manually downloaded the Data from Kaggle. You can download the Data from here. I have used Google Colab for this Project so the act of reading and loading the Data might be different in various other platforms.
• The Dataset contains 43 different types of Traffic Signs. The overview of the each Signs are summarized below:

Index	Traffic Signs
0	Speed Limit (20km/h)
1	Speed Limit (30km/h)
2	Speed Limit (50km/h)
3	Speed Limit (60km/h)
4	Speed Limit (70km/h)
5	Speed Limit (80km/h)
6	End of Speed Limit (80km/h)
7	Speed Limit (100km/h)
8	Speed Limit (120km/h)
9	No Passing
10	No Passing for the Vehicles over 3.5 metric tons
11	Right of way at next intersection
12	Priority Road
13	Yield
14	Stop
15	No Vehicles
16	Vehicles over 3.5 metric tons prohibited
17	No Entry
18	General Caution
19	Dangerous curve to the left
20	Dangerous curve to the right
21	Double Curve
22	Bumpy Road
23	Slippery Road
24	Road narrows on the right
25	Road Work
26	Traffic Signals
27	Pedestrains
28	Children Crossing
29	Bicycles Crossing
30	Beware of Ice/Snow
31	Wild animals crossing
32	End of all speed and Passing limits
33	Turn right ahead
34	Turn left ahead
35	Ahead Only
36	Go straight or right
37	Go straight or left
38	Keep Right
39	Keep Left
40	Round about Mandatory
41	End of no Passing
42	End of no Passing by vehicles over 3.5 metric tons.

Snapshot of the Input Images

Convolutional Neural Network

• In deep learning, a convolutional neural network is a class of deep neural networks, most commonly applied to analyzing visual imagery. They are also known as shift invariant or space invariant artificial neural networks, based on their shared-weights architecture and translation invariance characteristics.
• Convolutional Neural Network are fully connected feed forward Neural Networks. Convolutional Neural Networks are very effective in reducing the number of parameters with losing on the quality of the models. Images have the high dimensionality which suits the above described abilities of the Convolutional Neural Networks.

model = tf.keras.models.Sequential([
                                    tf.keras.layers.Conv2D(16, (3, 3), activation="relu", input_shape=(32, 32, 1)),
                                    tf.keras.layers.MaxPooling2D(2, 2),
                                    tf.keras.layers.Conv2D(32, (3, 3), activation="relu"),
                                    tf.keras.layers.MaxPooling2D(2, 2),
                                    tf.keras.layers.Conv2D(64, (3, 3), activation="relu"),
                                    tf.keras.layers.MaxPooling2D(2, 2),
                                    tf.keras.layers.Dropout(0.2),
                                    tf.keras.layers.Flatten(),
                                    tf.keras.layers.Dense(512, activation="relu"),
                                    tf.keras.layers.Dense(120, activation="relu"),
                                    tf.keras.layers.Dense(84, activation="relu"),
                                    tf.keras.layers.Dense(43, activation="softmax")                                   
])

Model Evaluation

• Plotting Training Accuracy vs Validation Accuracy

• As the Training Accuracy increases then the Validation Accuracy also increases.

• Plotting Training Loss vs Validation Loss

• As the Training Loss decreases then the Validation Loss also decreases.

Snapshot of the Predicted Image