Open Journal Systems

Sistem Keamanan Gerbang Parkir Menggunakan Algoritma YOLO (You Only Look Once) dan Face Recognition

       Tata Supriyadi, Kemal Taufik Fikri

Abstract


Saat ini, sistem parkir masih menggunakan sistem parkir yang bersifat manual, dimana proses keluar masuknya kendaraan masih menggunakan pengecekan STNK atau karcis sehingga memungkinkan resiko kesalahan manusia dan rentan akan keamanan. Agar masalah ini dapat dikurangi, maka pada penelitian ini dibuatlah suatu sistem parkir cerdas pada barrier gate yang mana mampu mengidentifikasi wajah pengendara dan plat nomornya pada saat akan memasuki area parkir. Pada penelitian kali ini digunakan kecerdasan buatan menggunakan algoritma You Only Look Once (YOLO) untuk identifikasi dan pengenalan karakter plat nomor serta library face recognition untuk mengekstrak fitur pada wajah pengendara. Sistem berisi bagian yang mengontrol pembukaan gerbang keamanan pada bagian pintu keluar. Apabila wajah pengendara pada pintu keluar tidak cocok dengan pintu keluar maka sistem tidak mengizinkan gerbang keamanan untuk dibuka. Berdasarkan hasil penelitian yang telah dilakukan terhadap 372 gambar deteksi plat dan 169 gambar untuk pengenalan karakter didapat tingkat keberhasilan deteksi plat nomor sebesar 91,86% dengan average IOU 74,15% dan pengenalan karakter mencapai 95,8% dengan average IOU mencapai 81,25%. Adapun total total waktu komputasi sistem selama 4,85 detik pada Google Colabs dan 37,69 detik menggunakan Jetson Nano.


  http://dx.doi.org/10.31544/jtera.v7.i2.2022.189-200

Keywords


parkir cerdas; kecerdasan buatan; YOLO; face recognition; barrier gate

Full Text:

  PDF

References


Badan Pusat Statistik, “Statistik Kriminal 2020,” Badan Pusat Statistik, Jakarta, 2020.

G. S. Hsu, A. Ambikapathi, S. L. Chung, and C. P. Su, “Robust license plate detection in the wild,” 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1-6, 2017.

M. A. Rafique, W. Pedrycz, and M. Jeon, “Vehicle license plate detection using region-based convolutional neural networks,” Soft Computing, vol. 22, no. 19, pp. 6429-6440, 2018.

G. Subhashini, M. E. Markhi, and R. Abdulla, “Automatic Car Park Management System Using Face and Vehicle Registration Recognition,” in Proceedings of The Fourth International Conference on Technological Advances in Electrical, Electronics and Computer Engineering, Malaysia, 2016.

K. Jain, T. Choudhury, and N. Kashyap, “Smart vehicle identification system using OCR,” in 2017 3rd international conference on computational intelligence & communication technology (CICT), Ghaziabad, 2017.

C. H. Lin, Y. S. Lin, and W. C. Liu, “An efficient license plate recognition system using convolution neural networks,” in 2018 IEEE International Conference on Applied System Invention (ICASI), Chiba, pp. 224-227, 2018.

Z. Mahmood, T. Ali, S. Khattak, S. U. Khan, and L. T. Yang, “Automatic Vehicle Detection and Driver Identification Framework for Secure Vehicle Parking,” in 2015 13th International Conference on Frontiers of Information Technology (FIT), Islamabad, 2015.

I. Taleb, M. E. A. Ouis, and M. O. Mammar, “Access control using automated face recognition: Based on the PCA & LDA algorithms,” in 2014 4th International Symposium ISKO-Maghreb: Concepts and Tools for knowledge Management (ISKO-Maghreb), Algiers, pp. 1-5, 2015.

M. Y. Aalsalem, W. . Z. Khan, and K. M. Dhabbah, “An automated vehicle parking monitoring and management system using ANPR cameras,” in 2015 17th International Conference on Advanced Communication Technology (ICACT), Pyeong Chang, pp. 706-710, 2015.

N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection,” Lecture Notes in Computer Science, pp. 428-441, 2006.

A. Geitgey, “Machine Learning is Fun! Part 4: Modern Face Recognition with Deep Learning,” Medium, 24 July 2016. [Online]. Available: https://medium.com/@ageitgey/machine-learning-is-fun-part-4-modern-face-recognition-with-deep-learning-c3cffc121d78.

A. Rosebrock, “OpenCV Thresholding ( cv2.threshold ),” pyimagesearch, 28 April 2021. [Online]. Available: https://pyimagesearch.com/2021/04/28/opencv-thresholding-cv2-threshold/.

S. Yohanandan, “mAP (mean Average Precision) might confuse you!,” Towards Data Science, 9 June 2020. [Online]. Available: https://towardsdatascience.com/map-mean-average-precision-might-confuse-you-5956f1bfa9e2.

S. Narkhede, “Accuracy, Recall, Precision, F-Score & Specificity, which to optimize on?,” Towards Data Science, 2 April 2019. [Online]. Available: https://towardsdatascience.com/accuracy-recall-precision-f-score-specificity-which-to-optimize-on-867d3f11124.

A. K. Jain and S. Z. Li, “Handbook of face recognition,” Springer, vol. I, 2011.




DOI: http://dx.doi.org/10.31544/jtera.v7.i2.2022.189-200
Abstract 36 View    PDF viewed = 13 View

Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 JTERA (Jurnal Teknologi Rekayasa)

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Copyright @2016-2023 JTERA (Jurnal Teknologi Rekayasa) p-ISSN 2548-737X e-ISSN 2548-8678.

     Lisensi Creative Commons

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

 

JTERA Editorial Office:
Politeknik Sukabumi
Jl. Babakan Sirna 25, Sukabumi 43132, West Java, Indonesia
Phone/Fax: +62 266215417
Whatsapp: +62 81809214709
Website: https://jtera.polteksmi.ac.id
E-mail: jtera@polteksmi.ac.id