1. Road Traffic Sign Recognition
ECE 539 Project
Road Traffic Sign Recognition
Bo Peng

2. Background & Motivation
Traffic signs ensure the safety when people drive
Automatic recognition for autonomous driving
(DataCamp)

3. Data Belgian Traffic Signs Dataset (64 categories )
Training: 4575 images / Testing: 2520 images

4. Data German Traffic Signs Dataset (43 categories )
Training: 39,209 images / Testing: 12,630 images

5. Method Data transformation
Resize all the images into the same size, e.g., 64 × 64
Normalize the greyscale of the images to [0, 1]
Divide training data into training part and validation part
Size of validation data: 20%
Convolutional neural network + fully connected layers

6. Results and Discussion
Training process
Epoch: 1...Train acc: 50.0%...Validation acc: 62.5%...Validation loss: 1.245
Epoch: 2...Train acc: 90.6%...Validation acc: 90.6%...Validation loss: 0.245
Epoch: 3...Train acc: 96.9%...Validation acc: 96.9%...Validation loss: 0.117
Epoch: 4...Train acc: 100.0%...Validation acc: 96.9%...Validation loss: 0.183
Epoch: 5...Train acc: 100.0%...Validation acc: 100.0%...Validation loss: 0.029
Epoch: 6...Train acc: 96.9%...Validation acc: 90.6%...Validation loss: 0.179
Acc = percentage of images being correctly recognized
Loss = softmax cross entropy between truth and predictions
Epoch 6 is over-fitting

7. Results and Discussion
Belgian Traffic Signs
German Traffic Signs
Image size
Num of epochs
Batch size
Testing acc 64 7 16
89.7% 32
86.0%
79.8%
Image size
Num of epochs
Batch size
Testing acc 32 6 16
89.7%
87.0% 5
88.0%