DETEKSI DAN PENGENALAN PLAT NOMOR KENDARAAN INDONESIA MENGGUNAKAN METODE DEEP LEARNING BERBASIS YOLOv8 DAN CONVOLUTIONAL NEURAL NETWORK
DOI:
https://doi.org/10.29407/h0dzra45Abstract
Perkembangan teknologi kecerdasan buatan telah mendorong munculnya berbagai sistem otomatis dalam bidang transportasi dan keamanan, salah satunya adalah Automatic Number Plate Recognition (ANPR). Sistem ini berfungsi untuk mendeteksi dan mengenali plat nomor kendaraan secara otomatis melalui pemrosesan citra digital. Penelitian ini bertujuan mengembangkan sistem deteksi dan pengenalan plat nomor kendaraan Indonesia menggunakan metode You Only Look Once versi 8 (YOLOv8) dan Convolutional Neural Network (CNN). YOLOv8 digunakan untuk mendeteksi lokasi plat nomor pada citra kendaraan, sedangkan CNN digunakan untuk mengenali karakter huruf dan angka yang terdapat pada plat nomor. Dataset yang digunakan terdiri atas citra kendaraan roda dua dan roda empat dengan berbagai kondisi pencahayaan, sudut pengambilan gambar, dan jarak kamera. Tahapan penelitian meliputi pengumpulan dataset, preprocessing data, pelatihan model YOLOv8, segmentasi karakter, pelatihan model CNN, serta evaluasi sistem menggunakan parameter akurasi, precision, recall, F1-score, dan waktu pemrosesan. Hasil penelitian menunjukkan bahwa kombinasi YOLOv8 dan CNN mampu memberikan performa yang baik dalam mendeteksi dan mengenali plat nomor kendaraan secara otomatis. Sistem yang dikembangkan berpotensi diterapkan pada sistem parkir otomatis, pengawasan lalu lintas, dan sistem keamanan berbasis computer vision.
Keywords:
CNN, Deep Learning, License Plate Recognition, YOLOv8, Computer Vision.##plugins.themes.default.displayStats.downloads##
References
[1] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, RealTime Object Detection,” in Proceedings of the IEEE Conference on Computer Vision and
Pattern Recognition (CVPR), 2016, pp. 779–788, doi:10.1109/CVPR.2016.91.
[2] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep
Convolutional Neural Networks,” Communications of the ACM, vol. 60, no. 6, pp. 84–90,
2017, doi:10.1145/3065386.
[3] N. H. Harani, C. Prianto, and M. Hasanah, “Deteksi Objek dan Pengenalan Karakter Plat
Nomor Kendaraan Indonesia Menggunakan CNN Berbasis Python,” Jurnal Informatika, vol.
6, no. 1, pp. 45–54, 2019.
[4] A. Aprilino and I. H. Al Amin, “Implementasi Algoritma YOLO dan Tesseract OCR pada
Sistem Deteksi Plat Nomor Otomatis,” Jurnal Teknologi Informasi, vol. 7, no. 2, pp. 112–
120, 2022.
[5] W. Setiawan and N. H. Farhan, “Deteksi Objek Plat Nomor Kendaraan Menggunakan
Metode CNN,” Jurnal Komputasi, vol. 8, no. 1, pp. 15–22, 2022.
[6] S. Christanti, K. N. Ekamartha, and A. P. Sari, “Implementasi Sistem Pengenalan Plat Nomor
Kendaraan Lokal Menggunakan CNN,” Jurnal Sistem Cerdas, vol. 3, no. 2, pp. 88–97, 2024.
[7] S. L. Chang, L. S. Chen, Y. C. Chung, and S. W. Chen, “Automatic License Plate
Recognition,” IEEE Transactions on Intelligent Transportation Systems, vol. 5, no. 1, pp.
42–53, doi:10.1109/TITS.2004.825086.
[8] M. Laroca et al., “A Robust Real-Time Automatic License Plate Recognition Based on the
YOLO Detector,” International Joint Conference on Neural Networks (IJCNN), pp. 1–10,
2018, doi:10.1109/IJCNN.2018.8489629.
[9] A. Du, H. Wang, and X. Li, "Vehicle License Plate Detection Based on Improved YOLO
Network," IEEE Access, vol. 10, pp. 105432–105444, 2022,
doi:10.1109/ACCESS.2022.3209876.
[10] Z. Zhang, Y. Li, and J. Wang, "Automatic License Plate Recognition Using Deep Learning
and Computer Vision Techniques," Sensors, vol. 23, no. 4, Art. no. 2015, 2023,
doi:10.3390/s23042015.
[11] Y. Wang, J. Liu, and H. Zhang, "Real-Time License Plate Detection Based on YOLOv8 for
Intelligent Transportation Systems," Applied Sciences, vol. 14, no. 2, Art. no. 678, 2024,
doi:10.3390/app14020678.
[12] R. Silva and K. Jung, "Real-Time Brazilian License Plate Detection and Recognition Using
Deep Convolutional Neural Networks," in SIBGRAPI Conference on Graphics, Patterns and
Images, 2017, pp. 55–62, doi:10.1109/SIBGRAPI.2017.14.
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