Sistem Pembacaan Plat Nomor Otomatis untuk Kendali Akses Parkir Menggunakan YOLOv8 dan Tesseract
Abstrak
Abstrak — Sistem parkir konvensional menghadapi tantangan dalam efisiensi dan keamanan, terutama dalam pencatatan data dan verifikasi akses manual. Untuk mengatasi masalah ini, dikembangkan sebuah simulasi sistem parkir cerdas berbasis Internet of Things (IoT) yang mengintegrasikan teknologi Automatic Number Plate Recognition (ANPR) sebagai mekanisme verifikasi utama untuk pembukaan palang otomatis. Sistem ini memanfaatkan model deteksi objek YOLOv8 untuk mengidentifikasi plat nomor kendaraan dan Tesseract OCR untuk mengekstrak karakternya. Pengujian dilakukan pada prototipe skala miniatur yang menggunakan Raspberry Pi 4 sebagai pusat kendali dan kamera webcam. Fokus pengujian meliputi akurasi pembacaan plat nomor format 7 dan 8 digit serta waktu pemrosesan yang dibutuhkan. Hasil simulasi menunjukkan bahwa sistem ANPR mampu menjalankan fungsinya secara efektif dalam lingkungan terkendali, dengan rata-rata waktu pemrosesan per plat nomor berada dalam kisaran 3-4 detik, meskipun akurasi bervariasi antara format plat. Analisis ini membuktikan kelayakan pendekatan berbasis machine learning sebagai fondasi teknis untuk sistem parkir otomatis yang efisien dan aman di masa mendatang. Kata kunci— ANPR, YOLOv8, Tesseract OCR, Raspberry Pi, Simulasi, Parkir Otomatis
Unduhan
Referensi
L. Wang, J. Chen, C. Zhang, X. Cao, and J. Chen, “Vehicle delay model applied to dynamic and static traffic impact analysis of large parking lots,”
Applied Sciences Switzerland, vol. 11, no. 20, 2021, doi: 10.3390/app11209771.
T. P. Hong, A. C. Soh, H. Jaafar, and A. J. Ishak, “Real-time monitoring system for parking space management services,” in Proceedings - 2013 IEEE
Conference on Systems, Process and Control,
ICSPC 2013, 2013, pp. 149–153. doi: 1109/SPC.2013.6735122.
A. Badr, M. M. Abdel, A. M. Thabet, and A. M. Abdelsadek, “Automatic number plate recognition system,” Annals of the University of Craiova Mathematics and Computer Science Series, vol. 38, no. 1, pp. 62–71, 2011.
N. Bhaskar, P. T. Waghmare, A. K. Puttur, and O. V. Pereira, Advanced smart parking management system integrating deep learning and optical character recognition. 2025. doi: 1201/9781003616252-59.
I. Valova, T. Kaneva, and N. Valov, “Conceptual
Model of a Parking System with Automatic License
Plate Recognition,” in IEEE International Symposium for Design and Technology of Electronics Packages, SIITME - Conference Proceedings, 2024, pp. 168–173. doi: 1109/SIITME63973.2024.10814907.
M. Talib, J. Suad, and A. H. Y. Al-Noori, “YOLOv8-CAB: Improved YOLOv8 for Real-time
Object Detection,” Karbala International Journal of Modern Science, vol. 10, no. 1, pp. 56–68, 2024, doi: 10.33640/2405-609X.3339.
A. Tupsounder, R. Patwari, R. Ambati, A.
Chavarkar, and K. Shirsat, “Automatic Recognition of Non-standard Number Plates using YOLOv8,” in Proceedings of the 18th INDIAcom; 2024 11th
International Conference on Computing for Sustainable Global Development, INDIACom 2024, , pp. 314–319. doi: 23919/INDIACom61295.2024.10498740.
M. Pallawabonang, M. Adnan, A. Mukmin, S.
Zuhriyah, E. Prakasa, and Yuyun, “Leveraging YOLOv8 for Real-Time Parking Space Detection,” in 2024 Beyond Technology Summit on Informatics
International Conference Bts I2c 2024, 2024, pp.
–444. doi: 10.1109/BTSI2C63534.2024.10942123.
D. Zhang et al., “Optimization of YOLOv8 model based on pruning,” in Proceedings of SPIE the International Society for Optical Engineering, 2024. doi: 10.1117/12.3049687.
F. Lubis et al., “Integrated Smart Parking System Using Internet of Things (IoT) and Digital Image Processing with Faster Region Based Convolutional Neural Network (Faster R-CNN) and Optical Character Recognition (OCR),” in Proceedings - ELTICOM 2024: 8th International Conference on
Electrical, Telecommunication and Computer Engineering: Tech-Driven Innovations for Global Organizational Resilience, 2024, pp. 342–348. doi: 1109/ELTICOM64085.2024.10864954.
H. Santhi, G. Gopichand, and P. Gayathri, “Automated smart parking system using IoT,”
Journal of Advanced Research in Dynamical and Control Systems, vol. 10, no. 9 Special, pp. 1110– , 2018.
C. Ajchariyavanich et al., “Park king: An IoT-based smart parking system,” in 5th IEEE International Smart Cities Conference, ISC2 2019, 2019, pp. 729– doi: 10.1109/ISC246665.2019.9071721.
N. Shaikh, H. Shah, and H. Sharma, IoT-Based Smart Car Parking Agent Using Raspberry Pi, vol. 2024. doi: 10.1007/978-981-97-1323-3_18.
N. Anggraini, A. F. Zein, L. K. Wardhani, and K. Fadhillah, “Smart Parking System With Background
Subtraction Algorithm Using Progressive Web Apps (PWAs) Technology on Raspberry Pi 4,” in 2023
th International Conference on Cyber and IT
Service Management Citsm 2023, 2023. doi: 1109/CITSM60085.2023.10455254.
K. V. Jobin, C. V. Jiji, and P. R. Anurenjan, “Automatic number plate recognition system using modified stroke width transform,” in 2013 4th
National Conference on Computer Vision, Pattern
Recognition, Image Processing and Graphics, NCVPRIPG 2013, 2013. doi: 1109/NCVPRIPG.2013.6776246.
M. Yulianti, C. Suhery, I. Ruslianto, J. Sistem Komputer, and F. H. MIPA Universitas Tanjungpura Jl Hadari Nawawi, “PENDETEKSI TEMPAT PARKIR MOBIL KOSONG MENGGUNAKAN METODE CANNY,” 2017.
