QR code detection using convolutional neural networks

Chou, Tzu-Han; Ho, Chuan-Sheng; Kuo, Yan-Fu

doi:10.1109/aris.2015.7158354

Cited by 28 publications

(25 citation statements)

References 12 publications

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“…Compared with Ref. [36], the proposed MU R-CNN has a higher recall rate. In Table 8, n is the total number of images in the test data set, and t is the time used for detection.…”

Section: Compare With the Relevant Qr Location Algorithmmentioning

confidence: 88%

“…Li [34] use morphological methods, but the speed is slow. Grósz [35] and Chou [36] use the neural network (NN) method, and achieve good effect. Lin [37] use HOG and Adaboost to locate the location of QR codes.…”

Section: Related Workmentioning

confidence: 99%

“…From the above references, it can be noticed that Lin [37] and Yuan [38] belongs to the general object detection method and Ref. [31][32][33][34][35][36] to the dedicated QR code detection method. These dedicated QR code detection methods are also designed on the basis of the general object detection principle.…”

Section: Related Workmentioning

confidence: 99%

“…For the Data set D 2 and D 3 , similar as the data sets used in Ref. [36] and [31,34], they can be used to compare the algorithm proposed in this paper with those in relevant literatures. In Ref.…”

Section: Instance Segmentation Experimentsmentioning

confidence: 99%

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MU R-CNN: A Two-Dimensional Code Instance Segmentation Network Based on Deep Learning

Yuan

Li²,

Jiang

et al. 2019

Future Internet

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In the context of Industry 4.0, the most popular way to identify and track objects is to add tags, and currently most companies still use cheap quick response (QR) tags, which can be positioned by computer vision (CV) technology. In CV, instance segmentation (IS) can detect the position of tags while also segmenting each instance. Currently, the mask region-based convolutional neural network (Mask R-CNN) method is used to realize IS, but the completeness of the instance mask cannot be guaranteed. Furthermore, due to the rich texture of QR tags, low-quality images can lower intersection-over-union (IoU) significantly, disabling it from accurately measuring the completeness of the instance mask. In order to optimize the IoU of the instance mask, a QR tag IS method named the mask UNet region-based convolutional neural network (MU R-CNN) is proposed. We utilize the UNet branch to reduce the impact of low image quality on IoU through texture segmentation. The UNet branch does not depend on the features of the Mask R-CNN branch so its training process can be carried out independently. The pre-trained optimal UNet model can ensure that the loss of MU R-CNN is accurate from the beginning of the end-to-end training. Experimental results show that the proposed MU R-CNN is applicable to both high-and low-quality images, and thus more suitable for Industry 4.0.

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“…Compared with Ref. [36], the proposed MU R-CNN has a higher recall rate. In Table 8, n is the total number of images in the test data set, and t is the time used for detection.…”

Section: Compare With the Relevant Qr Location Algorithmmentioning

confidence: 88%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Instance Segmentation Experimentsmentioning

confidence: 99%

See 2 more Smart Citations

MU R-CNN: A Two-Dimensional Code Instance Segmentation Network Based on Deep Learning

Yuan

Li²,

Jiang

et al. 2019

Future Internet

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“…Next, a space transformation to correct image distortion is applied, achieving precision of recognition about 98.57% with the average time equal to 38 ms. Similarly, the proposal of Chou et al in [34] consists in localization and segmentation of QR codes with Hough transform, applying then a convolutional neural network used for symbol recognition in images with blur, rotation, and uneven illumination reaching correct detection rate about 95.2%. More similar to the proposed approach, in this paper, it consists of multistage classifier based on Hough transform that improves QR code detection by relying only on the geometry of symbol and its deterministic properties without using complex filters [28].…”

Section: Modern Techniques For Qr Code Detection and Recognitionmentioning

confidence: 99%

Binary Large Object-Based Approach for QR Code Detection in Uncontrolled Environments

López-Rincón

Starostenko

Alarcón-Aquino

et al. 2017

Journal of Electrical and Computer Engineering

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Quick Response QR barcode detection in nonarbitrary environment is still a challenging task despite many existing applications for finding 2D symbols. The main disadvantage of recent applications for QR code detection is a low performance for rotated and distorted single or multiple symbols in images with variable illumination and presence of noise. In this paper, a particular solution for QR code detection in uncontrolled environments is presented. The proposal consists in recognizing geometrical features of QR code using a binary large object-(BLOB-) based algorithm with subsequent iterative filtering QR symbol position detection patterns that do not require complex processing and training of classifiers frequently used for these purposes. The high precision and speed are achieved by adaptive threshold binarization of integral images. In contrast to well-known scanners, which fail to detect QR code with medium to strong blurring, significant nonuniform illumination, considerable symbol deformations, and noising, the proposed technique provides high recognition rate of 80%-100% with a speed compatible to real-time applications. In particular, speed varies from 200 ms to 800 ms per single or multiple QR code detected simultaneously in images with resolution from 640 × 480 to 4080 × 2720, respectively.

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Personalized Mobile Learning System via Smart Glasses

Tsai

Chen

et al. 2018

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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QR code detection using convolutional neural networks

Cited by 28 publications

References 12 publications

MU R-CNN: A Two-Dimensional Code Instance Segmentation Network Based on Deep Learning

MU R-CNN: A Two-Dimensional Code Instance Segmentation Network Based on Deep Learning

Binary Large Object-Based Approach for QR Code Detection in Uncontrolled Environments

Personalized Mobile Learning System via Smart Glasses

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