Traffic Sign Classification Using Deep and Quantum Neural Networks
This work addresses traffic sign recognition for autonomous driving systems, but it is incremental as it shows quantum neural networks are promising but not yet superior to classical methods.
The paper tackled traffic sign classification by implementing a hybrid quantum-classical convolutional neural network, achieving over 90% accuracy on the German Traffic Sign Recognition Benchmark dataset, though it did not outperform classical deep convolutional neural networks.
Quantum Neural Networks (QNNs) are an emerging technology that can be used in many applications including computer vision. In this paper, we presented a traffic sign classification system implemented using a hybrid quantum-classical convolutional neural network. Experiments on the German Traffic Sign Recognition Benchmark dataset indicate that currently QNN do not outperform classical DCNN (Deep Convolutuional Neural Networks), yet still provide an accuracy of over 90% and are a definitely promising solution for advanced computer vision.