Traffic Sign Classification Using Deep and Quantum Neural Networks
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 5 of its 22 references that are in the map. First posted on arXiv 30 Sep 2022.
Builds on
5 of 22 references.
References · 5
- 2018Quantum generative adversarial networkscited by 128
- 2019Transfer learning in hybrid classical-quantum neural networkscited by 114
- 2021Hybrid Classical-Quantum Deep Learning Models for Autonomous Vehicle Traffic Image Classification Under Adversarial Attackcited by 6
- 2021Comparing concepts of quantum and classical neural network models for image classification taskcited by 5
- 2020Image Classification via Quantum Machine Learningcited by 3
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Citing papers per year: 2026: 2.
2 papers.