The Effects of Vertical and Horizontal Distance on The Performance of QR Code Detection System

Hakim, Moad; Yatmono, Sigit; Nugraha, Ariadie Chandra; Khairudin, Moh.

doi:10.1088/1742-6596/1737/1/012029

Cited by 8 publications

(3 citation statements)

References 2 publications

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“…Judges also conducted experiments regarding the maximum vertical and horizontal distance of the quadcopter in detecting the presence of the QR Code. The results of this study show that the maximum vertical distance to detect QR Codes is 115 cm, while the vertical distance is 155 cm [15].…”

Section: Figure 7 Monitor Display Of Detection Results On Qrmentioning

confidence: 75%

Autonomous Quadcopter With Image Object Detection Method as a Sender of Assistance for Covid-19 Patients

2021

IJOMAM

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In its development, image processing is very helpful for solving problems that are often faced by humans in general. Image processing is used for technological purposes, especially in the field of computer vision. In this research, the image processing system is applied as a QR code detector in a quadcopter. The quadcopter will detect the presence of a QR Code, after the Qr Code is detected the mechanical system on the quadcopter will drop objects, in this case, medicines for Covid-19 patients, at a predetermined point. The steps needed to complete this research are the design and assembly of the quadcopter, black-box testing, and direct testing of the quadcopter sending medicinal aid. The design and assembly process is carried out in the robotics laboratory of Yogyakarta State University. The black box testing process is used to test the performance of the software in the quadcopter, which uses 3 different types of Qr Code. The experimental testing process is used to test the electronic and mechanical circuits in the quadcopter, whether the circuit can work properly or not. The software used in this research is python IDLE 3.6. The black box testing results show that the system is functioning optimally, where the quadcopter can detect all existing Qr Codes. Whereas in the experimental results, the quadcopter can drop objects at predetermined locations. Based on this test, it can be concluded that a quadcopter can be applied to send medical aid to Covid-19 patients without being controlled/automated.

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Section: Figure 7 Monitor Display Of Detection Results On Qrmentioning

confidence: 75%

Autonomous Quadcopter With Image Object Detection Method as a Sender of Assistance for Covid-19 Patients

2021

IJOMAM

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“…After converting the captured image into the gray one followed by binarization, [24] obtains the QR code's bounding-box by locating the Finder Pattern of the QR code. [25] also converts the RGB image into a binary one and then performs morphological processing to detect the QR code. [26] proposes a QR code detection approach based on image integration, which uses the integral image to determine a threshold for binarization, and then applies the Finder Pattern of the QR code to locate the QR code through the extracted connection regions.…”

Section: A Image Processing-based Approachmentioning

confidence: 99%

“…3 are two intersected boxes. The width iou w , height iou h [18][19][20][21] Mainly using the Hough transform to detectiong QR codes [22] Applying a kind of structure matrix in HSV color space to locate the QR code [23] Detecting the boundary of QR code after binarization [24] Having a better performance in uneven lighting environments [24] Locating the QR code via the Finder Pattern [25] Applying morphological processing to images to find QR codes [26] Finding the QR code by the Finder Pattern after image integration Deep learning-based [29] Using YOLOv2 to detect the positions of QR codes [31] Obtaining the rotation angle of QR code by a Multi-Layer Perceptron Network [32] Locating QR code by YOLOv2 following the line detection algorithm [33] Proposing an end-to-end model to detect QR codes and IoU score of their intersected region can be defined as…”

Section: A Multistage Stepwise Discrimination 1) Analysis Of Predicte...mentioning

confidence: 99%

Rapid Detection of Multi-QR Codes Based on Multistage Stepwise Discrimination and a Compressed MobileNet

Rong-jun

Huang

et al. 2023

IEEE Internet Things J.

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Poor real-time performance in multi-QR codes detection has been a bottleneck in QR code decoding based Internet-of-Things (IoT) systems. To tackle this issue, we propose in this paper a rapid detection approach, which consists of Multistage Stepwise Discrimination (MSD) and a Compressed MobileNet. Inspired by the object category determination analysis, the preprocessed QR codes are extracted accurately on a small scale using the MSD. Guided by the small scale of the image and the end-to-end detection model, we obtain a lightweight Compressed MobileNet in a deep weight compression manner to realize rapid inference of multi-QR codes. The Average Detection Precision (ADP), Multiple Box Rate (MBR) and running time are used for quantitative evaluation of the efficacy and efficiency. Compared with a few state-of-the-art methods, our approach has higher detection performance in rapid and accurate extraction of all the QR codes. The approach is conducive to embedded implementation in edge devices along with a bit of overhead computation to further benefit a wide range of real-time IoT applications.

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

Pratomo,

Thariq,

Prasetyo

2024

E3S Web Conf.

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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.

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The Effects of Vertical and Horizontal Distance on The Performance of QR Code Detection System

Cited by 8 publications

References 2 publications

Autonomous Quadcopter With Image Object Detection Method as a Sender of Assistance for Covid-19 Patients

Autonomous Quadcopter With Image Object Detection Method as a Sender of Assistance for Covid-19 Patients

Rapid Detection of Multi-QR Codes Based on Multistage Stepwise Discrimination and a Compressed MobileNet

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

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