Raspberry Pi based Automatic Door Control System

Selvaraj, Kailasam; Alagarsamy, Saravanan; Dhilipkumar, M

doi:10.1109/icspc51351.2021.9451687

Cited by 15 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Oleh karena itu, tujuan dari penelitian ini untuk mengembangkan AGV dengan memanfaatkan Raspberry Pi 4, yang memiliki kemampuan dalam memproses gambar secara real-time dengan cepat dan akurat. Karena itu, penerapan pengolahan citra dapat dijalankan pada sistem AGV sehingga dapat beroperasi dengan [12].…”

Section: Pendahuluanunclassified

Penerapan Deteksi Garis Pada Agv Menggunakan Metode HSV

Saputra,

Setiawan

2023

Transmisi J.Ilm. Tek. Elekt.

View full text Add to dashboard Cite

Dalamperkembangan teknologisaatini,khususnya disektorindustri,robotdengankecerdasanbuatanmengemban peranpentingdalammeningkatkan efisiensiwaktukerjamanusia.PenelitianinimenciptakansebuahrobotAGVyang dilengkapisistemkecerdasanbuatan,menggunakan kamerasebagaisensorvisualuntukmendeteksi garislintasan sebagaipanduannavigasi.RobotAGVinimemanfaatkanOpenCVuntukpemrosesancitradenganmetodefilterwarna HSV.Metodeinimencakup teknikmorfologi danGaussianbluruntukmengenali garislintasanyangakandilalui. Setelah prosesidentifikasijalur, robot AGV akan bergerak sesuai jalur yang terdeteksioleh kamera.Pengujian perangkatkeras dilakukandi laboratorium,bahwamodenavigasirobotAGV berdasarkanmetode HSV mampu berfungsibaikdanmenghasilkantingkatakurasideteksijaluryangtinggi.

show abstract

Section: Pendahuluanunclassified

Penerapan Deteksi Garis Pada Agv Menggunakan Metode HSV

Saputra,

Setiawan

2023

Transmisi J.Ilm. Tek. Elekt.

View full text Add to dashboard Cite

show abstract

“…In [1][2][3][4][5][6][7][8], they show that the biometric technology has been widely adopted in access control systems. In contrast to the RFID technology [9], the biometric technology cannot provide all correct results for user identification, especially identifying with a large number of people, meaning that there might be some false results of user identification [10].…”

Section: Introductionmentioning

confidence: 99%

An Access Control System using RFID and Face Verification with QR Code-based Enrollment

2023

j.it

View full text Add to dashboard Cite

The objective of this paper is to propose the system architecture and implementation of an access control system using RFID technology and face verification. The implemented system was employed to control user's access at the tripod turnstile gate in the library of the Prince of Songkla University (Trang Campus). The access control system is divided into two parts: enrolment and access control. In the enrolment part, the system obtains the user information from three processes:(1) the basic user information (e.g., full name and university identification number) is received via a SOAP web service,(2) the face image of a user is detected by using the camera of a mobile phone, (3) the UID data of a RFID card is read in a form of QR code by using our enrolment board.In the access control part, the RFID reader board forwards the UID data reading from an RFID card into the main controller board via RS-232 communication. The main controller board is used to send the UID data into the Flask web server in order to retrieve the corresponding face image encoding, and then verify with faces that are detected from the webcam at the tripod turnstile gate. The main controller board also controls the tripod turnstile gate via the push-pull solenoid and receives external signals from the proximity sensor and limit switch. The performance of the implemented system using the Raspberry Pi 4 Model B board as the main controller board in terms of verification accuracy and response time was measured, and also compared with the NVIDIA Jetson Nano board.

show abstract

“…The Raspberry Pi was employed in various face recognition applications because of its superiority and portability ( Gunawan et al, 2017 ), a door unlocking system using Raspberry Pi ( Vamsi, Sai & Vijayalakshmi, 2019 ; Selvaraj, Alagarsamy & Dhilipkumar, 2021 ), Sajjad et al (2020) suggested a system that helps the police to find missing persons and suspects using wireless cameras, Hasban et al (2019) proposed an attendance system for the classroom, Ambre, Masurekar & Gaikwad (2020) produced a face recognition system using Raspberry Pi that can be used by governments to save money for CCTV monitoring staff ( Gaikwad, 2020 ), Majid et al (2021) proposed a PCA method for face recognition, but the best recognition rate they achieved was 82.5%.…”

Section: Introductionmentioning

confidence: 99%

Fast and accurate face recognition system using MORSCMs-LBP on embedded circuits

Hosny

Hamad

Elkomy

et al. 2022

PeerJ Computer Science

View full text Add to dashboard Cite

Because of the current COVID-19 circumstances in the world and the tremendous technological developments, it has become necessary to use this technology to combat the spread of the new coronavirus. The systems that depend on using hands, such as fingerprint systems and PINs in ATM systems, could lead to infection, so they have become undesirable and we can replace them by using facial recognition instead. With the development of technology and the availability of nano devices like the Raspberry Pi, such applications can be implemented easily. This study presents an efficient face recognition system in which the face image is taken by a standalone camera and then passed to the Raspberry Pi to extract the face features and then compare them with the database. This approach is named MORSCMs-LBP by combining two algorithms for feature extraction: Local Binary Pattern (LBP) as a local feature descriptor and radial substituted Chebyshev moments (MORSCMs) as a global feature descriptor. The significant advantage of this method is that it combines the local and global features into a single feature vector from the detected faces. The proposed approach MORSCMs-LBP has been implemented on the Raspberry Pi 4 computer model B with 1 GB of RAM using C++ OpenCV. We assessed our method on various benchmark datasets: face95 with an accuracy of 99.0278%, face96 with an accuracy of 99.4375%, and grimace with 100% accuracy. We evaluated the proposed MORSCMs-LBP technique against other recently published approaches; the comparison shows a significant improvement in favour of the proposed approach.

show abstract

Raspberry Pi based Automatic Door Control System

Cited by 15 publications

References 14 publications

Penerapan Deteksi Garis Pada Agv Menggunakan Metode HSV

Penerapan Deteksi Garis Pada Agv Menggunakan Metode HSV

An Access Control System using RFID and Face Verification with QR Code-based Enrollment

Fast and accurate face recognition system using MORSCMs-LBP on embedded circuits

Contact Info

Product

Resources

About