Traffic Sign Classification Using Deep and Quantum Neural Networks

• URL: http://arxiv.org/abs/2209.15251v1
• Date: Fri, 30 Sep 2022 06:16:03 GMT
• Title: Traffic Sign Classification Using Deep and Quantum Neural Networks
• Authors: Sylwia Kuros, Tomasz Kryjak
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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