Detection of Vehicle License Plates Using YOLO11: Deteksi Tanda Nomor Kendaraan Bermotor menggunakan Algoritma YOLO11 - Samuel Meinus Untu; Salvius P. Lengkong; Muhamad Dwisnanto Putro (Universitas Sam Ratulangi, 2026)
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)