Traffic Sign Classification Using Deep and Quantum Neural Networks

TL;DR

This paper explores a hybrid quantum-classical neural network for traffic sign classification, achieving over 90% accuracy on a benchmark dataset, highlighting its potential despite current limitations compared to classical deep neural networks.

Contribution

It introduces a novel hybrid quantum-classical CNN architecture for traffic sign recognition and evaluates its performance on a standard dataset.

Findings

QNNs achieve over 90% accuracy on traffic sign classification

QNNs currently do not outperform classical CNNs

Hybrid quantum-classical models show promise for future computer vision applications

Abstract

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.