The scheme of robot positioning based on multiple quick response(QR)code landmarks is proposed.Firstly,the pose of the robot relative to the QR code landmarks is obtained by extracting the feature points of the QR cod...The scheme of robot positioning based on multiple quick response(QR)code landmarks is proposed.Firstly,the pose of the robot relative to the QR code landmarks is obtained by extracting the feature points of the QR code,combined with the visual computing technology.Then,the conversion of the absolute pose of the robot is completed based on the absolute position of the QR code landmark,and thus the real-time position of the robot is obtained.The proposed scheme makes full use of the QR code fault tolerance and multiple QR code landmarks to improve the calculation accuracy.It has good robustness and versatility,and it is easy to implement.It can help the robot to complete the positioning in the actual work,making robot navigation more accurate.展开更多
This study introduces a lightweight deep learning model and a novel synthetic dataset designed to restore damaged one-dimensional(1D)barcodes and Quick Response(QR)codes,addressing critical challenges in logistics ope...This study introduces a lightweight deep learning model and a novel synthetic dataset designed to restore damaged one-dimensional(1D)barcodes and Quick Response(QR)codes,addressing critical challenges in logistics operations.The proposed solution leverages an efficient Pix2Pix-based framework,a type of conditional Generative Adversarial Network(GAN)optimized for image-to-image translation tasks,enabling the recovery of degraded barcodes and QR codes with minimal computational overhead.A core contribution of this work is the development of a synthetic dataset that simulates realistic damage scenarios frequently encountered in logistics environments,such as low contrast,misalignment,physical wear,and environmental interference.By training on this diverse and realistic dataset,the model demonstrates exceptional performance in restoring readability and decoding accuracy.The lightweight architecture,featuring a U-Net-based encoder-decoder with separable convolutions,ensures computational efficiency,making the approach suitable for real-time deployment on embedded and resource-constrained devices commonly used in logistics systems.Experimental results reveal significant improvements:QR code decoding ratios increased from 14%to 99%on training data and from 15%to 68%on validation data,while 1D barcode decoding ratios improved from 7%to 73%on training data and from 9%to 44%on validation data.By providing a robust,resource-efficient solution for restoring damaged barcodes and QR codes,this study offers practical advancements for enhancing the reliability of automated scanning systems in logistics operations,particularly under challenging conditions.展开更多
In order to increase the watermarking capacity and improve the anti-counterfeiting performance of quick response （QR） two-dimensional （2D） barcode based on digital watermarking technique, this paper proposes a kin...In order to increase the watermarking capacity and improve the anti-counterfeiting performance of quick response （QR） two-dimensional （2D） barcode based on digital watermarking technique, this paper proposes a kind of graying algorithm for binary image to make transform domain watermarking methods possible. Based on the graying Mgorithm, the quantizatiombased watermarking scheme in discrete wavelet transform （DWT） domain is then designed. The experiments show that the proposed watermarking scheme for QR 2D barcode greatly improves anti-counterfeiting performance without loss of any barcode information.展开更多
Recently,both the importance and awareness relating to food safety relating to agrochemicals,chemical fertilizers,the pollution of ecosystem and unwholesome foods are growing rapidly.For the same reason,the purchaser&...Recently,both the importance and awareness relating to food safety relating to agrochemicals,chemical fertilizers,the pollution of ecosystem and unwholesome foods are growing rapidly.For the same reason,the purchaser's curiosity increases to know about the production process which can be accomplished by production traceability system.The traceability system can store and superintend the specific attribute information about products regarding the productive centre,producer and production finish data.The products undergo various circulation processes and they are being managed by individually constructed traceability system.Thus,it is difficult for the producer and the consumer to guarantee the safety from transpirable issues about the falsification of production traceability because the traceability information can be re-produced by forging through various ways.So in this paper,we proposed a system for integrated management of agro-fishery products based on quick response code(QR-code)for forgery prevention which may help to solve these issues.The proposed system makes QR-code using production information provided by producer.And then it is provided to the distributor and the consumer through integrated service model using the QR-code.Therefore,this system make sure to provide the same production traceability about the products to the distributor and the consumer and always keeps both updated with the same information.展开更多
This paper briefly introduces the characteristics and structure of symbol QR two-dimensional code, a detailed analysis of the image processing method to identify QR code of the whole process, and the bilinear mapping ...This paper briefly introduces the characteristics and structure of symbol QR two-dimensional code, a detailed analysis of the image processing method to identify QR code of the whole process, and the bilinear mapping method is applied to image correction, the final steps of decoding are given. The actual test results show that, the design algorithm has theoretical and practical, this recognition system can correctly read QR code, and has high recognition rate and recognition speed, has practical value and application prospect.展开更多
QR codes are widely used in applications such as information sharing,advertising,and digital payments.However,their growing adoption has made them attractive targets for malicious activities,including malware distribu...QR codes are widely used in applications such as information sharing,advertising,and digital payments.However,their growing adoption has made them attractive targets for malicious activities,including malware distribution and phishing attacks.Traditional detection approaches rely on URL analysis or image-based feature extraction,whichmay introduce significant computational overhead and limit real-time applicability,and their performance often depends on the quality of extracted features.Previous studies in malicious detection do not fully focus on QR code securitywhen combining convolutional neural networks(CNNs)with recurrent neural networks(RNNs).This research proposes a deep learning model that integrates AlexNet for feature extraction,principal component analysis(PCA)for dimensionality reduction,and RNNs to detect malicious activity in QR code images.The proposed model achieves both efficiency and accuracy by transforming image data into a compact one-dimensional sequence.Experimental results,including five-fold cross-validation,demonstrate that the model using gated recurrent units(GRU)achieved an accuracy of 99.81%on the first dataset and 99.59%in the second dataset with a computation time of only 7.433 ms per sample.A real-time prototype was also developed to demonstrate deployment feasibility.These results highlight the potential of the proposed approach for practical,real-time QR code threat detection.展开更多
In this paper a label-based simultaneous localization and mapping（ SLAM） system is proposed to provide localization to indoor autonomous robots. In the system quick response（ QR） codes encoded with serial numbers ...In this paper a label-based simultaneous localization and mapping（ SLAM） system is proposed to provide localization to indoor autonomous robots. In the system quick response（ QR） codes encoded with serial numbers are utilized as labels. These labels are captured by two webcams,then the distances and angles between the labels and webcams are computed. Motion estimated from the two rear wheel encoders is adjusted by observing QR codes. Our system uses the extended Kalman filter（ EKF） for the back-end state estimation. The number of deployed labels controls the state estimation dimension. The label-based EKF-SLAM system eliminates complicated processes,such as data association and loop closure detection in traditional feature-based visual SLAM systems. Our experiments include software-simulation and robot-platform test in a real environment. Results demonstrate that the system has the capability of correcting accumulated errors of dead reckoning and therefore has the advantage of superior precision.展开更多
基金Project of Introducing Urgently Needed Talents in Key Support Areas of Shandong Province,China。
文摘The scheme of robot positioning based on multiple quick response(QR)code landmarks is proposed.Firstly,the pose of the robot relative to the QR code landmarks is obtained by extracting the feature points of the QR code,combined with the visual computing technology.Then,the conversion of the absolute pose of the robot is completed based on the absolute position of the QR code landmark,and thus the real-time position of the robot is obtained.The proposed scheme makes full use of the QR code fault tolerance and multiple QR code landmarks to improve the calculation accuracy.It has good robustness and versatility,and it is easy to implement.It can help the robot to complete the positioning in the actual work,making robot navigation more accurate.
基金supported by the Scientific and Technological Research Council of Turkey(TÜB˙ITAK)through the Industrial R&D Projects Grant Program(TEYDEB)under Project No.3211077(grant recipient:Metin Kahraman)。
文摘This study introduces a lightweight deep learning model and a novel synthetic dataset designed to restore damaged one-dimensional(1D)barcodes and Quick Response(QR)codes,addressing critical challenges in logistics operations.The proposed solution leverages an efficient Pix2Pix-based framework,a type of conditional Generative Adversarial Network(GAN)optimized for image-to-image translation tasks,enabling the recovery of degraded barcodes and QR codes with minimal computational overhead.A core contribution of this work is the development of a synthetic dataset that simulates realistic damage scenarios frequently encountered in logistics environments,such as low contrast,misalignment,physical wear,and environmental interference.By training on this diverse and realistic dataset,the model demonstrates exceptional performance in restoring readability and decoding accuracy.The lightweight architecture,featuring a U-Net-based encoder-decoder with separable convolutions,ensures computational efficiency,making the approach suitable for real-time deployment on embedded and resource-constrained devices commonly used in logistics systems.Experimental results reveal significant improvements:QR code decoding ratios increased from 14%to 99%on training data and from 15%to 68%on validation data,while 1D barcode decoding ratios improved from 7%to 73%on training data and from 9%to 44%on validation data.By providing a robust,resource-efficient solution for restoring damaged barcodes and QR codes,this study offers practical advancements for enhancing the reliability of automated scanning systems in logistics operations,particularly under challenging conditions.
基金the National Natural Science Foundation of China(No.60903203)the Fujian Educational Bureau JK Projects(No.JK2012044)the Xiamen Municipal Science and Technology Plan Projects(No.3502Z20103036)
文摘In order to increase the watermarking capacity and improve the anti-counterfeiting performance of quick response （QR） two-dimensional （2D） barcode based on digital watermarking technique, this paper proposes a kind of graying algorithm for binary image to make transform domain watermarking methods possible. Based on the graying Mgorithm, the quantizatiombased watermarking scheme in discrete wavelet transform （DWT） domain is then designed. The experiments show that the proposed watermarking scheme for QR 2D barcode greatly improves anti-counterfeiting performance without loss of any barcode information.
基金The MSIP(Ministry of Science,ICT&Future Planning),Korea,under the ITRC(Information Technology Research Center)support program(NIPA-2013-H0301-13-2006)supervised by the NIPA(National IT Industry Promotion Agency)
文摘Recently,both the importance and awareness relating to food safety relating to agrochemicals,chemical fertilizers,the pollution of ecosystem and unwholesome foods are growing rapidly.For the same reason,the purchaser's curiosity increases to know about the production process which can be accomplished by production traceability system.The traceability system can store and superintend the specific attribute information about products regarding the productive centre,producer and production finish data.The products undergo various circulation processes and they are being managed by individually constructed traceability system.Thus,it is difficult for the producer and the consumer to guarantee the safety from transpirable issues about the falsification of production traceability because the traceability information can be re-produced by forging through various ways.So in this paper,we proposed a system for integrated management of agro-fishery products based on quick response code(QR-code)for forgery prevention which may help to solve these issues.The proposed system makes QR-code using production information provided by producer.And then it is provided to the distributor and the consumer through integrated service model using the QR-code.Therefore,this system make sure to provide the same production traceability about the products to the distributor and the consumer and always keeps both updated with the same information.
文摘This paper briefly introduces the characteristics and structure of symbol QR two-dimensional code, a detailed analysis of the image processing method to identify QR code of the whole process, and the bilinear mapping method is applied to image correction, the final steps of decoding are given. The actual test results show that, the design algorithm has theoretical and practical, this recognition system can correctly read QR code, and has high recognition rate and recognition speed, has practical value and application prospect.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University Jeddah,under grant no.(GPIP:1168-611-2024)The authors acknowledge the DSR for financial and technical support.
文摘QR codes are widely used in applications such as information sharing,advertising,and digital payments.However,their growing adoption has made them attractive targets for malicious activities,including malware distribution and phishing attacks.Traditional detection approaches rely on URL analysis or image-based feature extraction,whichmay introduce significant computational overhead and limit real-time applicability,and their performance often depends on the quality of extracted features.Previous studies in malicious detection do not fully focus on QR code securitywhen combining convolutional neural networks(CNNs)with recurrent neural networks(RNNs).This research proposes a deep learning model that integrates AlexNet for feature extraction,principal component analysis(PCA)for dimensionality reduction,and RNNs to detect malicious activity in QR code images.The proposed model achieves both efficiency and accuracy by transforming image data into a compact one-dimensional sequence.Experimental results,including five-fold cross-validation,demonstrate that the model using gated recurrent units(GRU)achieved an accuracy of 99.81%on the first dataset and 99.59%in the second dataset with a computation time of only 7.433 ms per sample.A real-time prototype was also developed to demonstrate deployment feasibility.These results highlight the potential of the proposed approach for practical,real-time QR code threat detection.
基金Supported by Program for Changjiang Scholars and Innovative Research Team in University,National Science Foundation of China(61105092)the National Natural Science Foundation of China(61473042)
文摘In this paper a label-based simultaneous localization and mapping（ SLAM） system is proposed to provide localization to indoor autonomous robots. In the system quick response（ QR） codes encoded with serial numbers are utilized as labels. These labels are captured by two webcams,then the distances and angles between the labels and webcams are computed. Motion estimated from the two rear wheel encoders is adjusted by observing QR codes. Our system uses the extended Kalman filter（ EKF） for the back-end state estimation. The number of deployed labels controls the state estimation dimension. The label-based EKF-SLAM system eliminates complicated processes,such as data association and loop closure detection in traditional feature-based visual SLAM systems. Our experiments include software-simulation and robot-platform test in a real environment. Results demonstrate that the system has the capability of correcting accumulated errors of dead reckoning and therefore has the advantage of superior precision.
基金supported by the National Natural Science Foundation of China (21971185 and 52173177)the Natural Science Foundation of Jiangsu Province (BK20230010 and BK20221362)+4 种基金the Science and Technology Support Program of Jiangsu Province (TJ-2022-002)funded by the Collaborative Innovation Center of Suzhou Nano Science&Technologythe 111 Projectthe Joint International Research Laboratory of Carbon-Based Functional Materials and DevicesSoochow University Tang Scholar。
