Detection and rectification method for bent QR code recognition using convolutional neural networks
Detection and rectification method for bent QR code recognition using convolutional neural networks
复制标题
基于卷积神经网络的弯曲二维码识别检测与矫正方法
DOI：
10.1088/2631-8695/acb67e
复制
发表时间：
2023
影响因子：
1.7
通讯作者：
Tanaka Kazumoto
中科院分区：
文献类型：
--
作者：
Genki Sakata;Naoshi Kaneko;Dai Hasegawa and Shinichi Shirakawa;古家 一樹，市村 真希，高田 秀志;内藤 大輝，市村 真希，高田 秀志;Tanaka Kazumoto
本文提出了一种解码附着在圆柱体上的弯曲快速响应码的方法。该方法包括使用基于有限元方法的变形分析中使用的形状函数和 pix2pix 网络（一种生成对抗网络）进行两阶段图像校正。基于形状函数的校正需要弯曲代码的八个特征点，称为节点。堆叠沙漏网络（一种用于人体姿势估计的卷积神经网络）用于检测这八个节点。实验结果表明，与其他方法相比，该方法能够更准确地解码曲率较大的弯曲码。
This paper proposes a method for decoding a bent quick-response code attached to a cylinder. The proposed method consists of two-stage image rectification using the shape function employed in a finite-element-method-based deformation analysis and a pix2pix network, which is a type of generative adversarial network. Rectification based on the shape function requires eight feature points, called nodes, of the bent code. A stacked hourglass network, a convolutional neural network used for human pose estimation, is used to detect these eight nodes. The experimental results show that, compared with other methods, the proposed method can more accurately decode bent codes with larger degrees of curvature.