QR Code Recognition Research Articles

Application of digital model of mixed reality dynamic tracking technique in oral and maxillofacial surgery: a basic research.

This paper aims to explore the application value of mixed reality in oral and maxillofacial surgery and to conduct dynamic tracking using an in vitro model. By collecting preoperative enhanced CT data of patients, rebuilding 3D digital model, combined with 3D printing technology, dynamic tracking of lesions was realized in the in vitro model, and the efficiency of different registration methods was compared. The 3D visualized head and neck model was obtained by combining multiple anatomical models, and dynamic tracking was completed in vitro. The average tracking time of the facial mark recognition method was T45°=3.67 frames, T90°=10.67 frames, and T total=12 seconds 28 frames (30 frames per second). The average tracking time of QR code recognition method was T45°=1.67 frames, T90°=2.33 frames, and T total=11 seconds 13 frames (30 frames per second). The combination of MR technology and 3D printing technology can realize the dynamic tracking of lesions in vitro, which lays a foundation for the clinical application of MR technology to implement precise, personalized surgical programs.

Empowering Sustainable Household Waste Management through Rubbin: App-Based Transactions using Google Maps API and QR Code Recognition

The Digitization of environmentally friendly technology must be applied to advance smart cities. Waste management carried out conventionally often causes irregularities in the classification data collection, difficulties in accessing information related to garbage collection schedules, and a lack of transaction history information, which causes a decrease in the quality of waste collection. The development of functional application features is urgently needed as a container for proper waste management. The Rubbin App has been created, which implements Google Maps API for route optimization and waste mapping, QR code recognition using Enhanced Adaptive Median Filter, and private chat for clients and collectors as additional features. The purpose of designing this research is to overcome the impacts of climate change, significantly reducing gas emissions, by proposing a digital waste management system by providing iOS and Android multiplatform applications with attractive and easy-to-use UI/UX interfaces. The Rubbin application utilizes Google Maps API technology to enable waste tracking and route optimization while employing QR codes to streamline the transaction process between clients and collectors, ensuring efficient waste management operations. The Rubbin application results show a change in the community's waste management habits, forming a culture of caring for the environment.

High-Performance Embedded System Design for QR Code Recognition With Deep Learning

High-Performance Embedded System Design for QR Code Recognition With Deep Learning

An Algorithm for the Recognition of Motion-Blurred QR Codes Based on Generative Adversarial Networks and Attention Mechanisms

Motion blur can easily affect the quality of QR code image, making it difficult to recognize QR codes on moving objects. This paper proposes an algorithm for the recognition of motion-blurred QR codes based on generative adversarial network and attention mechanism. Firstly, a multi-scale feature extraction framework for motion defuzzification is designed using deep convolutional neural networks, and enhanced multi-scale residual blocks and multi-scale feature extraction modules are utilized to capture rich local and global features. Secondly, the efficient channel attention module is added to strengthen the weights of effective features and suppress invalid features by modeling the correlations between channels. In addition, training stability is achieved through the use of the WGAN-div loss function, leading to the generation of higher quality samples. Finally, the proposed algorithm is evaluated through qualitative and quantitative comparisons with several recent methods on both the GOPRO public dataset and a self-constructed QR code dataset, respectively. The experimental results demonstrate that, compared to the other methods, the proposed algorithm has shown significant improvements in both processing time and recognition accuracy when dealing with the task of severe motion-blurred QR code recognition.

QR Code Recognition Based on Image Processing

To solve the QR code recognition problem caused by ordinary camera collection, the recognition algorithm based on image processing is put forward in this paper. The whole process including image binarization, image tilt correction, image orientation, image geometric correction and image normalization allows images collected on different illumination conditions. Experiments show that the improved method can enhance the recognition speed of two-dimensional code and accuracy. QR i.e. “Quick Response” code is a 2D matrix code that is designed by keeping two points under consideration, i.e. it must store large amount of data as compared to 1D barcodes and it must be decoded at high speed using any handheld device like phones. QR code provides high data storage capacity, fast scanning, omnidirectional readability, and many other advantages including, error-correction (so that damaged code can also be read successfully) and different type of versions. Different varieties of QR code symbols like logo QR code, encrypted QR code, QR Code are also available so that user can choose among them according to their need. Now these days, a QR code is applied in different application streams related to marketing, security, academics etc. and gain popularity at a really high pace. Day by day more people are getting aware of this technology and use it accordingly. The popularity of QR code grows rapidly with the growth of smartphone users and thus the QR code is rapidly arriving at high levels of acceptance worldwide.

Drone navigation system based on QR code recognition using fast component-based two-staged algorithm

Kontes Robot Terbang Indonesia (KRTI) is a robotics competition organized by the National Achievement Center (Puspresnas). One of the branches of this competition is Vertical Takeoff and Landing (VTOL), which challenges participants to develop autonomous drones for package delivery based on QR code recognition. This research focuses on improving the accuracy of QR code recognition and drone navigation using a fast component-based two-stage method. Various image recognition methods for QR codes are discussed, including multi-threshold method, canny edge detection method, and fast componentbased two-stage method. The selected methods are modified to overcome the problems of rotation and perspective distortion. This research uses a quantitative and practical approach and is conducted indoors due to the limitations of drones on wind, light, and patterns on the ground. The fast component-based two-step method achieved 90.74% accuracy in QR code recognition, with certain challenges such as dark colored images. The navigation system relies on the drone’s position relative to the QR code in the image. The drone is instructed to approach the QR code for decoding. Test results showed 83.07% accuracy in matching commands, with problems due to QR code recognition failure. Strategies to overcome the recognition failure included rotation during takeoff and matching commands based on previous instructions. In addition, lag issues during drone navigation were addressed by optimizing the number of frames processed. The drone’s environmental requirements, such as well-lit areas and patterned floors, are described to ensure stable flight. This research provides insight into improving autonomous drone systems for package delivery through improved QR code recognition and navigation methods.

QR code recognition based on HOG and multiclass SVM classifier

QR code recognition based on HOG and multiclass SVM classifier

Autofocusing of Fresnel zone aperture lensless imaging for QR code recognition.

Fresnel zone aperture (FZA) lensless imaging encodes the incident light into a hologram-like pattern, so that the scene image can be numerically focused at a long imaging range by the back propagation method. However, the target distance is uncertain. The inaccurate distance causes blurs and artifacts in the reconstructed images. This brings difficulties for the target recognition applications, such as quick response code scanning. We propose an autofocusing method for FZA lensless imaging. By incorporating the image sharpness metrics into the back propagation reconstruction process, the method can acquire the desired focusing distance and reconstruct noise-free high-contrast images. By combining the Tamura of the gradient metrics and nuclear norm of gradient, the relative error of estimated object distance is only 0.95% in the experiment. The proposed reconstruction method significantly improves the mean recognition rate of QR code from 4.06% to 90.00%. It paves the way for designing intelligent integrated sensors.

AR 플랫폼을 위한 인터랙션 선정법 및 적용 사례 소개

AR technology is an essential technology to provide closer interaction with users in the metaverse and digital twin environments. Representative interaction methods for displaying content on the AR platform include image recognition, QR code recognition, NFC tag recognition, GPS signal, and Beacon recognition. In applying these various input methods, it is common for most developers to select and develop an input method empirically. In this paper, we examine the advantages and disadvantages of these input methods and implementation methods to reduce trial and error in the initial development of AR applications. In addition, we studies are presented to help find suitable AR interactions cases.

OPTIMIZATION OF FIXED ASSETS INVENTORY BY CREATING A TELEGRAM BOT

This article is about the optimization of fixed assets inventory by means of Telegram bot. The feature of the systems which are realized with the help of bots is the ease of use and minimization of resources spent on software storage, which greatly simplifies the interaction with the system. One of today’s popular platforms for deploying bots is the Telegram messenger.
 Purpose – create a tool for inventory of fixed assets, interaction with the database, creation of reports in Excel format and carrying out stock-taking with the help of QR code recognition.
 Method or methodology of the work. The article considers the method that implements the inventory of fixed assets and carrying out an inventory. For the implementation it is used: the SQLite database and the programming language Python.
 Result: developed a unique tool that implements the functions of adding, storing and interacting with the data of fixed assets.
 Scope of the results: object data stored in the database is reasonable to use for inventory and monitoring the current balance of fixed assets.

Detection and rectification method for bent QR code recognition using convolutional neural networks

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.

Two-Dimensional Codes Recognition Algorithm Based on Yolov5

In order to realize fast and high-precision recognition of QR codes, a QR code recognition method using YOLOv5 algorithm is designed. The original image of the QR code will be obtained by using the LabelImg annotation tool and data enhancement method to build a data set, and trained by the YOLOv5s model. The experimental results show that the test on the real two-dimensional code can be effectively identified, and the average accuracy rate is about 90%, which can meet the requirements of real-time identification of the two-dimensional code when using an augmented reality device.

Behavioral Based Credit Card Fraud Detection

Credit card fraud is a significant threat in the BFSI sector. This credit card fraud detection system analyzes user behavioral patterns and their location to identify any unusual patterns. This consists of user characteristics, which includes user spending styles as well as standard user geographic places to verify his identity. One of the user behavior patterns includes spending habits, usage patterns, etc. This system deals with user credit card data for various characteristics, which includes user country, usual spending procedures. Based upon previous transactions information of that person, the system recognizes unusual patterns in the payment method. The fraud detection system contains the past transaction data of each user. Based on this data, it identifies the standard user behavior patterns for individual users, and any deviation from those normal user patterns becomes a trigger for the detection system. If it detects any unusual patterns, then user will be required to undergo the security verification, which identifies the original user using QR code recognition system. In case of any unusual activity, the system not only raises alerts but it will block the user after three invalid attempts.

Research on QR image code recognition system based on artificial intelligence algorithm

Abstract The QR code recognition often faces the challenges of uneven background fluctuations, inadequate illuminations, and distortions due to the improper image acquisition method. This makes the identification of QR codes difficult, and therefore, to deal with this problem, artificial intelligence-based systems came into existence. To improve the recognition rate of QR image codes, this article adopts an improved adaptive median filter algorithm and a QR code distortion correction method based on backpropagation (BP) neural networks. This combination of artificial intelligence algorithms is capable of fitting the distorted QR image into the geometric deformation pattern, and QR code recognition is accomplished. The two-dimensional code distortion is addressed in this study, which was a serious research issue in the existing software systems. The research outcomes obtained after emphasizing on the preprocessing stage of the image revealed that a significant improvement of 14% is observed for the reading rate of QR image code, after processing by the system algorithm in this article. The artificial intelligence algorithm adopted has a certain effect in improving the recognition rate of the two-dimensional code image.

Design of Face QR Code Recognition System based on PCA

This article discusses the face two-dimensional code recognition technology, using the PCA algorithm to study the dimensionality reduction processing process of the face image. Based on the analysis of QR encoding and decoding methods, the generation of face two-dimensional codes is realized. Choose 140 pictures in ORL face database as the experimental data set, study the face two-dimensional code recognition system, and realize a process of face recognition. The experimental results show that the face image data has a large dimension, and the principal component analysis method can be used to obtain the key data. Through the experiment, PCA can be used to reduce the dimensionality to obtain the effective information of the face, and to encode and decode the two-dimensional code. The ZXing two-dimensional code open source library called by the experiment has high experimental efficiency, which is beneficial to improve the operating efficiency of face two-dimensional code recognition and facilitate the machine to complete the simulation.

Implementation of QR Code Recognition Technology Using Smartphone Camera for Indoor Positioning

Numerous studies on positioning technology are ongoing for recognizing the positions of objects accurately. Vision-, sensor-, and signal-based technologies are combined for recognizing the positions of objects outdoors and indoors. While positioning technologies involving wireless communication based on sensors and signals are commonly used in outdoor environments, the performance becomes degraded in indoor environments. Therefore, a vision-based indoor positioning method using a QR code is proposed in this study. A user’s position is measured by determining the current position of a smartphone device accurately based on the QR code recognized with a smartphone camera. The direction, distance, and position are acquired using the relationship between the three-dimensional spatial coordinate information of the camera and the center point coordinates of a two-dimensional planar QR code obtained through camera calibration.

Enhancing the Transparency of Student Merit System Using QR Code Technology: A Smart Campus Initiative

Technology has led to the utilization of information systems into human daily life activities. Computer systems have helped improve the modern human lifestyle by simplifying and facilitating activities from listening to music, grocery shopping to communicating with peoples across the world. Gearing up to smart campus initiatives, one of the issues to be tackled was the transparency in validating the student’s attendance system used in many campuses and university events. The employment of technologies such as QR Code and facial recognition to validate the student’s attendance certainly have helped in addressing this issue. In order to ensure the transparency of the existing manual student merit system used in the campus, this paper presents a solution known as CyberScan QR Code for Student Merit System that allows the students to obtain merits by scanning QR codes, as well as allowing student councils to provide merits for any events by generating QR codes. Students can keep track of their current total merit points and the events that they have previously attended so if there are any problems regarding their merits, they can always refer to their history of attended events as evidence. This paperless solution merit system also helps to reduce the usual time taken to calculate a huge amount of merits thus preventing from any miscalculation. Clearly, this initiative creates not just an efficient, transparent service but another greater experience for students to feel connected academically and socially.

Nurturing the culture of student participation through online reflection using the QR code system

Participation of student in reflection shows an important role in improving learning strategies. However, the common reflection practice focuses only on self-reflection and it becomes unauthentic. This innovation therefore attempted to prepare the platform for student’s reflection towards the teaching and learning activities in class using online reflection system. Hence an online reflection system based on QR code was developed to reflect this scenario. The code successfully accesses the system and allow the written reflection. Testing result shows the 100% efficiency of QR code recognition and excellence result for inter-rater reliability analysis by experts respectively.

改进灰狼优化算法及其在QR码识别上的应用

改进灰狼优化算法及其在QR码识别上的应用

Математичне та програмне забезпечення системи розпізнавання QR-коду візиток мобільними пристроями

Розглянуто проблеми використання QR-кодів. Проаналізовано контент, розміщений у QR-кодах, зазначено переваги та недоліки їх використання. Зазначено, що основними перевагами використання QR-кодів є простота читання їх мобільними пристроями та можливість відновлення інформації, розміщеної в QR-коді, за значних (до 30 %) пошкоджень коду. Основним же недоліком використання QR-коду є те, що розшифрувати його можна тільки за наявності спеціальних програм. Наведено безкоштовне програмне забезпечення для розпізнавання QR-коду. Зазначено перспективи використання QR-кодів у різноманітних напрямах впровадження. Для вирішення проблеми розміщення в QR-кодах додаткової інформації, зокрема зображення, відео, музики, презентацій тощо, запропоновано новий програмний інтерфейс (API), який складається з бази даних та серверної частини. Наведено функціональні можливості API. Використання розробленого API в мобільних додатках для будь-яких операційних систем дає змогу реалізувати єдину систему для зберігання додаткових даних, що на цей момент не використовується у жодній з систем розпізнавання QR-кодів. Розглянуто можливість застосування розробленого API для системи розпізнавання QR-коду візиток мобільними пристроями з ОС Android. Для створення такої системи проаналізовано алгоритми розшифрування QR-кодів, наведено її функціонал. Для створення веб-додатку використано трирівневу архітектуру. Наведено прототип додатку та форми, що забезпечують визначений функціонал.