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Received:May 22, 2024 Revised:June 28, 2024
Received:May 22, 2024 Revised:June 28, 2024
中文摘要: 针对 QR 码图像检测过程中因环境复杂性和拍摄角度变化等因素导致 QR 码读取率低的问题, 本文提出一种基于改进YOLOv8n-Pose的形变QR码校正与识别算法. 首先, 在主干网络引入高效通道注意力机制(efficient channel attention, ECA)模块, 该模块通过不降维的方式实现跨通道交互, 有效提升网络的特征提取能力和检测精度. 其次, 采用Slim-neck架构重构颈部网络, 减少模型的复杂度, 提高对不同尺度QR码的检测能力. 最后, 通过模型检测到的QR码角点, 结合逆透视变换对QR码进行校正, 并使用ZBar算法进行读取. 实验结果表明, 在公开的QR码数据集上, 改进的算法相比原算法, mAP50和mAP50-95分别提升1.6%和1.1%, 模型参数量和模型计算量分别降低6.5%和9.5%, 在CPU和GPU上检测速度分别提升0.3 f/s和0.7 f/s, 达到14.2 f/s和59.6 f/s, 能够高效地满足QR码角点检测需求. 此外, 在自制的形变QR 码数据集上, 基于改进YOLOv8n-Pose的QR码识别方法相比单独使用ZBar算法的QR 码识别方法, QR 码读取率提高23.66%, 达到87.41%. 该方法仅需拍摄一张照片就可识别所有货物的信息, 能够有效提高货物管理的效率.
中文关键词: QR码 关键点预测 YOLOv8n-Pose 注意力机制 定位与校正
Abstract:To address the problem of low QR code reading rates caused by complex environments and changes in shooting angles during QR code detection, this study proposes an algorithm for correcting and recognizing deformed QR codes based on an improved YOLOv8n-Pose algorithm. First, the efficient channel attention (ECA) module is introduced into the backbone network. This module achieves cross-channel interaction without dimensionality reduction, effectively enhancing the feature extraction capabilities and detection accuracy of the network. Secondly, the Slim-neck architecture is adopted to reconstruct the neck network, reducing model complexity and improving the detection capability for QR codes of different scales. Finally, detected QR code corner points are used for correction through inverse perspective transformation, and the corrected QR codes are read using the ZBar algorithm. Experimental results show that, on a public QR code dataset, the improved algorithm increases mAP50 and mAP50-95 by 1.6% and 1.1%, respectively, compared to the original algorithm. Model parameters and computational costs are reduced by 6.5% and 9.5%, respectively. Detection speed on CPU and GPU is improved by 0.3 f/s and 0.7 f/s, reaching 14.2 f/s and 59.6 f/s, respectively, meeting the requirements for efficient detection of QR code corner points. In addition, on a custom-made dataset of deformed QR codes, the proposed method based on the improved YOLOv8n-Pose algorithm enhances the QR code reading rate by 23.66% compared to the standalone ZBar algorithm, achieving a recognition rate of 87.41%. This method only requires one photo to recognize all the information about the goods, which can effectively improve the efficiency of goods management.
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基金项目:福建理工大学科研计划(GYZ20072)
引用文本:
刘云,邹复民,蔡祈钦,李俊清,钟继雄.改进YOLOv8n-Pose的形变QR码校正与识别.计算机系统应用,2024,33(12):141-152
LIU Yun,ZOU Fu-Min,CAI Qi-Qin,LI Jun-Qing,ZHONG Ji-Xiong.Deformed QR Code Correction and Recognition Based on Improved YOLOv8n-Pose.COMPUTER SYSTEMS APPLICATIONS,2024,33(12):141-152
刘云,邹复民,蔡祈钦,李俊清,钟继雄.改进YOLOv8n-Pose的形变QR码校正与识别.计算机系统应用,2024,33(12):141-152
LIU Yun,ZOU Fu-Min,CAI Qi-Qin,LI Jun-Qing,ZHONG Ji-Xiong.Deformed QR Code Correction and Recognition Based on Improved YOLOv8n-Pose.COMPUTER SYSTEMS APPLICATIONS,2024,33(12):141-152