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Erschienen in:
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2024 | OriginalPaper | Buchkapitel

Scanning QR Codes for Object Detection Based on Yolo-V7 Algorithm and Deblurring Generative Adversarial Network

verfasst von : Huan Chen, Hsin-Yao Hsu, Kuan-Ting Lin, Jia-You Hsieh, Yi-Feng Chang, Bo-Chao Cheng

Erschienen in: Frontier Computing on Industrial Applications Volume 4

Verlag: Springer Nature Singapore

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Abstract

Location-based advertising (LBA) has been popular for several years, and the amount of global investment is increasing year by year. Nowadays, in the vigorous development of vehicle vision systems, many recognition tasks can be completed by combining You Only Look Once version 7 (Yolo-v7) object detection algorithms to apply automotive applications, and also involve a QR codes decoding method with deblurring generative adversarial network version 2(DeblurGAN-v2), which can capture the QR codes set on the route in real-time to obtain the LBA placed by the merchant, the results show that the proposed method outperforms the other object detection model and deblurring model, it obtains more efficient for scanning QR codes.

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Vorheriges Kapitel Cluster-Based Blockchain Systems for Multi-access Edge Computing
Nächstes Kapitel Positive-Unlabeled Learning with Field of View Consistency for Histology Image Segmentation
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Metadaten
Titel
Scanning QR Codes for Object Detection Based on Yolo-V7 Algorithm and Deblurring Generative Adversarial Network
verfasst von
Huan Chen
Hsin-Yao Hsu
Kuan-Ting Lin
Jia-You Hsieh
Yi-Feng Chang
Bo-Chao Cheng
Copyright-Jahr
2024
Verlag
Springer Nature Singapore
Buch
Frontier Computing on Industrial Applications Volume 4

Print ISBN: 978-981-9993-41-3

Electronic ISBN: 978-981-9993-42-0

Copyright-Jahr: 2024

DOI
https://doi.org/10.1007/978-981-99-9342-0_13