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All Journal Jurnal Buana Informatika Majalah Ilmiah MOMENTUM Infotekmesin Jurnal Manajemen Informatika dan Sistem Informasi Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Ilmiah Intech : Information Technology Journal of UMUS Jurnal Pengabdian Kesehatan Masyarakat Mulawarman Journal of International Multidisciplinary Research Switch: Jurnal Sains dan Teknologi Informasi Inovasi Sosial: Jurnal Pengabdian Masyarakat
Yani Parti Astuti
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Nursing Public Health Social Sciences Other

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Evaluasi Performa YOLOv12 untuk Deteksi Plat Nomor Kendaraan Real-Time pada Citra Closed-Circuit Television (CCTV) Dicky Aristyanto; Yani Parti Astuti
Infotekmesin Vol 17 No 1 (2026): Infotekmesin: Januari 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v17i1.3097

Abstract

License plate detection is a crucial component of intelligent transportation systems. Deep learning methods still face limitations in detecting small-sized plates under low-light conditions and complex backgrounds. This study evaluates YOLOv12's performance for license plate detection in CCTV imagery containing small objects with great visual detail. Unlike YOLOv11, which focuses on detection efficiency for larger objects, YOLOv12 integrates attention mechanisms to enhance sensitivity to fine-grained spatial features. Model evaluation was conducted using precision, recall, and mean average precision (mAP) metrics on traffic image datasets with daytime and nighttime lighting conditions and CCTV viewing angles. Results show the model achieves mAP@0.5 of 87.2% and precision of 89.5%, comparable to previous YOLO-based studies. However, performance drops to 47.9% at mAP@0.5:0.95, indicating limitations in bounding-box localization precision under visually complex conditions. This study highlights opportunities for future improvement through dataset expansion and parameter optimization for training.
Co-Authors Abas Setiawan ahmad rizal Anggraini, Fitria Arya Erlangga Bambang Winardi Danny Oka Ratmana Defri Kurniawan Dewi Agustini Santoso Dewi Pergiwati Dicky Aristyanto Dwi Eko Waluyo Edi Noersasongko Edy Mulyanto Egia Rosi Subhiyakto Egia Rosi Subhiyakto Erlin Dolphina Erna Zuni Astuti Etika Kartikadarma Etika Kartikadarma Guruh Fajar Shidik Hayu Wikan Kinasih Nathaniel Alexander Naufal Fawwazi Novita Kurnia Ningrum Ramadhan Rakhmat Sani Rohman, Muhammad Syaifur Sindhu Rakasiwi T.Sutojo Utomo, Danang Wahyu Wibowo Wicaksono Winarsih, Nurul Anisa Sri