Crosswalk Traffic Light Detection for the Visually Impaired Based on Hybrid Classical-Quantum Neural Networks

Crosswalk Traffic Light Detection for the Visually Impaired Based on Hybrid Classical-Quantum Neural Networks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Crosswalk Traffic Light Detection for the Visually Impaired Based on Hybrid Classical-Quantum Neural Networks Jinghan Zhang, Zhengan Tian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5393740/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Jun, 2025 Read the published version in Quantum Information Processing → Version 1 posted 9 You are reading this latest preprint version Abstract This paper proposes a novel two-step method combining classical and quantum computation to accurately detect crosswalk traffic lights for visually impaired pedestrians. The first step utilizes the improved YOLOv5 model to identify crosswalk traffic light locations. In the second step, the detected lights are processed using a single-layer quantum convolutional neural network with a newly designed parameterized quantum circuit. The proposed quantum circuit reduces the number of qubits and circuit depth required while maintaining high accuracy, making it suitable for noisy intermediate-scale quantum (NISQ) devices. Experimental results demonstrate that our method has better performance compared to existing methods, achieving detection accuracy of 94.9%, precision of 95.4%, and the AUC reaches 0.98. These results also demonstrate the model’s robustness in complex traffic environments. Hybrid Classical-Quantum Neural Network YOLOv5 Algorithm Quantum Circuit Crosswalk Traffic Light Detection NISQ Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 19 Jun, 2025 Read the published version in Quantum Information Processing → Version 1 posted Editorial decision: Revision requested 17 May, 2025 Reviews received at journal 26 Mar, 2025 Reviewers agreed at journal 18 Mar, 2025 Reviews received at journal 12 Jan, 2025 Reviewers agreed at journal 30 Dec, 2024 Reviewers invited by journal 26 Dec, 2024 Editor assigned by journal 05 Nov, 2024 Submission checks completed at journal 05 Nov, 2024 First submitted to journal 05 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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