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<p>QR code is not only an information storage approach, but also a spatial localization sign. Compared to other spatial localization signs, QR code is more accurate and more efficient to be detected. To achieve spatial localization by QR code, detection is the essential procedure. Existing approaches perform well in regular light condition, but perform badly in complex light condition, because frame quality is extremely damaged by complex light condition. In the real world, complex light condition is very common but always unavoidable. Therefore, it is necessary and worthwhile to improve the under-complex-light QR code detection. In this paper, Vaccine-YOLOv10 (VCY) is proposed to enhance QR code detection capability in complex light condition. First, GhostConv and FasterC2f are introduced to replace the corresponding original modules of YOLOv10n. Second, Simulative Data Augment Algorithm (SDA) is proposed to simulate 5 types of complex light condition. Third, self-built Multi-Scene QR Code Dataset (MSQ) is augmented by SDA for VCY training. Compared to the baseline model YOLOv10n, VCY is improved on both lightweight and accuracy. Specifically, FPS reaches 150; GFLOPs reduces from 8.2 to 5.3; mAP50 increases from 0.877 to 0.905. Code: <a href="https://github.com/AlexTraveling/Vaccine-YOLOv10">https://github.com/AlexTraveling/Vaccine-YOLOv10</a>.</p>

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Vaccine-YOLOv10: real-time QR code detection model for complex light condition

  • 期刊论文

  • 发表日期：

    2025年3月11日

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  • Xiaobei Zhao,
  • Xiang Li

摘要

QR code is not only an information storage approach, but also a spatial localization sign. Compared to other spatial localization signs, QR code is more accurate and more efficient to be detected. To achieve spatial localization by QR code, detection is the essential procedure. Existing approaches perform well in regular light condition, but perform badly in complex light condition, because frame quality is extremely damaged by complex light condition. In the real world, complex light condition is very common but always unavoidable. Therefore, it is necessary and worthwhile to improve the under-complex-light QR code detection. In this paper, Vaccine-YOLOv10 (VCY) is proposed to enhance QR code detection capability in complex light condition. First, GhostConv and FasterC2f are introduced to replace the corresponding original modules of YOLOv10n. Second, Simulative Data Augment Algorithm (SDA) is proposed to simulate 5 types of complex light condition. Third, self-built Multi-Scene QR Code Dataset (MSQ) is augmented by SDA for VCY training. Compared to the baseline model YOLOv10n, VCY is improved on both lightweight and accuracy. Specifically, FPS reaches 150; GFLOPs reduces from 8.2 to 5.3; mAP50 increases from 0.877 to 0.905. Code: https://github.com/AlexTraveling/Vaccine-YOLOv10.