Szczegóły publikacji
Opis bibliograficzny
Traffic sign classification using deep and quantum neural networks / Sylwia Kuros, Tomasz KRYJAK // W: Computer Vision and Graphics : proceedings of the International Conference on Computer Vision and Graphics ICCVG 2022 : [19-21 September 2022, Warsaw] / eds. Leszek J. Chmielewski, Arkadiusz Orłowski. — Cham : Springer Nature Switzerland AG, cop. 2023. — (Lecture Notes in Networks and Systems ; ISSN 2367-3370 ; LNNS 598). — ISBN: 978-3-031-22024-1; e-ISBN: 978-3-031-22025-8. — S. 43–55. — Bibliogr., Abstr. — Publikacja dostępna online od: 2023-02-11
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 145405 |
|---|---|
| Data dodania do BaDAP | 2023-03-08 |
| DOI | 10.1007/978-3-031-22025-8_4 |
| Rok publikacji | 2023 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Czasopismo/seria | Lecture Notes in Networks and Systems |
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.
