Detection of Vehicle License Plates  Using YOLO11: Deteksi Tanda Nomor Kendaraan Bermotor menggunakan Algoritma YOLO11

Samuel Meinus Untu; Salvius P. Lengkong; Muhamad Dwisnanto Putro

doi:10.35793/jtek.v15i2.63538

Authors

Samuel Meinus Untu Universitas Sam Ratulangi
Salvius P. Lengkong Universitas Sam Ratulangi
Muhamad Dwisnanto Putro Universitas Sam Ratulangi

DOI:

https://doi.org/10.35793/jtek.v15i2.63538

Keywords:

YOLO11n, EasyOCR, object detection, character recognition, Automatic License Plate Recognition (ALPR)

Abstract

Abstract — An automatic vehicle license plate detection and recognition system was developed by integrating the YOLO11n algorithm with EasyOCR. The objective is to build a computer vision-based system capable of accurately detecting license plate positions and recognizing alphanumeric characters under real-world campus conditions at Sam Ratulangi University. The detection model achieved high performance with 97% precision, 98.4% recall, 97.7% F1-score, and 99% mAP@0.5. Character recognition using EasyOCR demonstrated near-zero Character Error Rate (CER) and Word Error Rate (WER) across most test images. Video testing with a three-hour duration recorded 1,757 successfully detected vehicles out of 2,866, resulting in a 61.3% detection accuracy. These results indicate that the integration of YOLO11n and EasyOCR provides an efficient, accurate, and adaptive solution for Automatic License Plate Recognition (ALPR) systems under varying lighting and plate orientation conditions.

Key words — YOLO11n; EasyOCR; object detection; character recognition; Automatic License Plate Recognition (ALPR)

Abstrak — Sistem deteksi dan pengenalan tanda nomor kendaraan bermotor otomatis dikembangkan dengan mengintegrasikan algoritma YOLO11n dan EasyOCR. Tujuannya adalah membangun sistem berbasis visi komputer yang mampu mendeteksi posisi plat nomor serta membaca karakter alfanumeriknya secara akurat pada kondisi nyata di lingkungan kampus Universitas Sam Ratulangi. Hasil pengujian menunjukkan performa deteksi tinggi dengan nilai precision sebesar 97%, recall 98,4%, F1-score 97,7%, dan mAP@0.5 99%. Proses pengenalan karakter melalui EasyOCR menunjukkan tingkat kesalahan rendah dengan nilai CER dan WER mendekati 0 pada sebagian besar citra uji. Pengujian video berdurasi tiga jam menunjukkan sistem mampu mendeteksi 1.757 dari total 2.866 kendaraan dengan tingkat keberhasilan 61,3%. Hasil tersebut membuktikan bahwa integrasi YOLO11n dan EasyOCR efektif diterapkan untuk sistem Automatic License Plate Recognition (ALPR) yang efisien, akurat, dan adaptif terhadap variasi pencahayaan serta orientasi plat nomor di lapangan.

Kata kunci — YOLO11n; EasyOCR; deteksi objek; pengenalan karakter; Automatic License Plate Recognition (ALPR)

References

[1] C. O. Y. Afita, “Penegakan hukum terhadap tindak pidana pemalsuan tanda nomor kendaraan bermotor (TNKB),” DATIN LAW JURNAL, vol. 2, no. 1, pp. 57–65, 2021.

[2] E. P. Silmina and R. A. Y. Arjun, “Pemanfaatan Model YOLOv8 Untuk Mendeteksi Plat Nomor Kendaraan Mobil Pada Gerbang Masuk Universitas XYZ,” Jurnal Sains dan Informatika, vol. 11, no. 1, pp. 50–59, 2025.

[3] M. D. Putro, D. -L. Nguyen and K. -H. Jo, "A Fast CPU Real-Time Facial Expression Detector Using Sequential Attention Network for Human–Robot Interaction," in IEEE Transactions on Industrial Informatics, vol. 18, no. 11, pp. 7665-7674, Nov. 2022

[4] N. Jegham, C. Y. Koh, M. Abdelatti, and A. Hendawi, “Evaluating the evolution of YOLO (You Only Look Once) models: A comprehensive benchmark study of YOLO11 and its predecessors,” arXiv preprint arXiv:2411, 2024.

[5] A. Sharma, V. Kumar, and L. Longchamps, “Comparative performance of YOLOv8, YOLOv9, YOLOv10, YOLOv11 and Faster R-CNN models for detection of multiple weed species,” Smart Agricultural Technology, vol. 9, p. 100648, 2024.

[6] S. Drobac and K. Lindén, “Optical character recognition with neural networks and post-correction with finite state methods,” International Journal on Document Analysis and Recognition (IJDAR), vol. 23, no. 4, pp. 279–295, 2020.

[7] S. A. Francis and M. Sangeetha, “A comparison study on optical character recognition models in mathematical equations and in any language,” Results in Control and Optimization, vol. 18, p. 100532, 2025.

[8] J. Bai, W. Zhu, Z. Nie, X. Yang, Q. Xu, and D. Li, “HFC-YOLO11: A Lightweight Model for the Accurate Recognition of Tiny Remote Sensing Targets,” Computers, vol. 14, no. 5, p. 195, 2025.

[9] M. Safran, A. Alajmi, and S. Alfarhood, “Efficient multistage license plate detection and recognition using YOLOv8 and CNN for smart parking systems,” Journal of Sensors, vol. 2024, p. 4917097, 2024.

[10] M. I. Wiawan, S. N. S. Nur, and W. S. G. Gumelar, “Prototipe Sistem Deteksi Dan Pengenalan Plat Nomor Sepeda Motor Di Indonesia Menggunakan YOLOv4,” Abditek Nusantara, vol. 5, no. 2, pp. 42–52, 2023.

[11] M. D. Putro, D.-L. Nguyen, and K.-H. Jo, “Lightweight Convolutional Neural Network for Real-Time Face Detector on CPU Supporting Interaction of Service Robot,” International Conference on Human System Interaction (HSI), Tokyo, Japan, 2020.

[12] X. Ji et al., “Ecological monitoring of invasive species through deep learning-based object detection,” Ecological Indicators, vol. 175, p. 113572, 2025.

[13] J. Vempati, “Real-time detection and recognition of license plate using YOLO11 object detection model,” Ph.D. dissertation, Kansas State University, 2024.

[14] S. Yang et al., “Fuzzy EfficientDet: An approach for precise detection of larch infestation severity in UAV imagery under dynamic environmental conditions,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 8810–8822, 2024.

[15] J. Shen, Z. Zhang, J. Luo, and X. Zhang, “YOLOv5-TS: Detecting traffic signs in real-time,” Frontiers in Physics, vol. 11, p. 1297828, 2023.

[16] A. Ignasius, J. C. Chandra, R. Oscadinata, and D. Suhartono, “Image Pre-Processing Effect on OCR's Performance for Image Conversion to Braille Unicode,” Procedia Computer Science, vol. 227, pp. 922–931, 2023.

[17] U. Poudel et al., “Applicability of OCR engines for text recognition in vehicle number plates, receipts and handwriting,” Journal of Circuits, Systems and Computers, vol. 32, no. 18, p. 2350321, 2023.