QHSA-ViT: A Quantum Discrete-Fourier-Transform-Based Hierarchical Self-Attention Fusion Vision Transformer for Traffic Sign Recognition in Intelligent Vehicular Networks

作者：Zhiguo Qu, Mengqing Zhou, Le Sun, Yimin Yu, Ghulam Muhammad · 发表于：IEEE Internet of Things Journal · 年份：2025 · DOI：10.1109/jiot.2025.3621725 · 被引用次数：3 · 研究领域：Brain Tumor Detection and Classification

With the rapid advancement of the intelligent Internet of Vehicles (IoV), accurate traffic sign classification is essential to ensure driving safety and improve environmental perception. However, conventional image classification models often rely on local features and spatial domain processing, lacking global context modeling and facing computational limitations. To address these challenges, this paper proposes a quantum discrete Fourier transform-based hierarchical self-attention Vision Transformer (QHSA-ViT). Using the parallelism and high-dimensional feature extraction capabilities of quantum computing, the proposed model enhances the quality and efficiency of representation. Specifically, a quantum frequency domain feature representation (QFDFR) module based on a quantum discrete Fourier transform (QDFT) is introduced to capture rich spectral features, while a quantum self-attention fusion (QSAF) module built on a linear combination of unitaries (LCU) and generalized quantum singular value transformation (GQSVT) integrates multilevel attention. The experimental results on five benchmark datasets, including GTSRB, show that QHSA-ViT outperforms baseline models with an average improvement of 9.01% in accuracy and 8.48% in the F1 score. These results validate the effectiveness of the proposed model and highlight its practical applicability and scalability for understanding traffic scenes in intelligent IoV.