Road Traffic Sign Recognition

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Presentation transcript:

Road Traffic Sign Recognition ECE 539 Project Road Traffic Sign Recognition Bo Peng bo.peng@wisc.edu

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

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

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

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

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

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%