Fast QR code detection based on BING and AdaBoost-SVM

Yuan, Baoxi; Li, Yang; Jiang, Fan; Xiaoli, Xu; Zhao, Jianhua; Zhang, Deyue; Guo, Jianxin; Wang, Yuqian; Zhang, Shanwen

doi:10.1109/hpsr.2019.8808000

Cited by 7 publications

(11 citation statements)

References 18 publications

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“…Lin [37] use HOG and Adaboost to locate the location of QR codes. In [38], we proposed a QR code positioning method based on BING and Adaboost-SVM.…”

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.…”

Section: Related Workmentioning

confidence: 99%

“…In order to compare with the "MU R-CNN + HMM" scheme proposed in Figure 7, we add HMM module to the algorithm in Ref. [38] for comparison. In Ref.…”

Section: Instance Segmentation Experimentsmentioning

confidence: 99%

“…In Ref. [38], we proposed a QR code detection algorithm based on BING and AdaBoost-SVM, which can quickly detect QR tags, but does not have the function of instance segmentation. Observations state v 0 and v 1 in formula (5) can be directly obtained by using the algorithm in Ref.…”

Section: Instance Segmentation Experimentsmentioning

confidence: 99%

“…Observations state v 0 and v 1 in formula (5) can be directly obtained by using the algorithm in Ref. [38], but v 2 cannot be directly obtained. Since v 2 represents the ratio of the white pixels number divided by black pixels number in binary mask image, we used the traditional Gaussian and Median filtering method to de-noise the detected target region, and then binarization by OTSU algorithm to obtain the v 2 state.…”

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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“…Lin [37] use HOG and Adaboost to locate the location of QR codes. In [38], we proposed a QR code positioning method based on BING and Adaboost-SVM.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…In order to compare with the "MU R-CNN + HMM" scheme proposed in Figure 7, we add HMM module to the algorithm in Ref. [38] for comparison. In Ref.…”

Section: Instance Segmentation Experimentsmentioning

confidence: 99%

Section: Instance Segmentation Experimentsmentioning

confidence: 99%

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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Intelligent predetection of projected reference markers for robot-based inspection systems

Bauer

Schmitt

Dirr

et al. 2022

Prod. Eng. Res. Devel.

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Technical advancements in optical devices like sensors and projectors have led to tremendous innovations in manufacturing metrology, not least due to reductions in cost and the use of sophisticated image processing software. More recently, methods based on machine learning have demonstrated their high potential in meeting challenges that are difficult to overcome using conventional image processing techniques. In this context, we present an approach for the intelligent predetection of projected reference markers in robot-based inspection systems. These markers support the alignment of different sensor views and do not need to be physically attached to any parts. However, their robust detection is challenging under unfavorable lighting conditions. Hence, we introduce trained models of a cascade classifier based on both synthetic and real image data. Subsequently, we present the detection performance for different shapes and designs of markers projected onto real-world sheet metal parts as used in the automotive industry. The results demonstrate that properly trained classifiers can achieve a recall and precision of 90% and higher. The use of intelligent predetection promises more robust results in the subsequent detection of projected markers and, thus, benefits image processing in particular in geometric quality assurance applications.

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Design of Prototype Information System for Tracking & Tracing Fish Distribution Based on Mobile Agent

Ardiansyah

Pusadan

Rombe

et al. 2022

IOP Conf. Ser.: Mater. Sci. Eng.

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The National Fish Logistics System or often called SLIN is an Indonesian Ministry of Maritime Affairs and Fisheries program that aims to maintain the stability of the production and marketing systems and control the disparity in national fish prices. Central Sulawesi is the Province that becomes the main corridor of this program. The inefficient distribution monitoring process generally causes several problems in the field of fisheries distribution management that still often occur today by the regional Ministry of Maritime Affairs and Fisheries. It indicates that SLIN is not yet running optimally. This study purpose a prototype design of fish distribution tracking based on a mobile agent that can use to help consumers to track distribution channels and get information about the origin of the fish to be purchased. The data will further process for monitoring fish distribution in a real-time manner by the regional Ministry of Maritime Affairs and Fisheries. A proper monitoring mechanism will undoubtedly help the government in making policies and conducting supervision to make the SLIN implementation successful in Central Sulawesi. By the research, we found that the proposed method can gather data from every level fish distribution agent then processed the data to inform about distribution line and the origin of the fish for the consumer. The proposed solution framework could be implemented and nearly fit with current implementation criteria. The framework later can be a base framework for developing a more advanced information system for SLIN in Central Sulawesi Region.

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Fast QR code detection based on BING and AdaBoost-SVM

Cited by 7 publications

References 18 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

Intelligent predetection of projected reference markers for robot-based inspection systems

Design of Prototype Information System for Tracking & Tracing Fish Distribution Based on Mobile Agent

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