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All Journal Jurnal Informatika Upgris SemanTIK : Teknik Informasi Jurnal INSYPRO (Information System and Processing) JSAI (Journal Scientific and Applied Informatics) Jurnal Manajemen Informatika dan Sistem Informasi International Journal of Engineering, Science and Information Technology Dikmas: Jurnal Pendidikan Masyarakat dan Pengabdian JOINCS (Journal of Informatics, Network, and Computer Science)
Suleman, Fitriyanti
STMIK ICHSAN GORONTALO
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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An Improvement of License Plate Detection Under Low Light Condition Using CLAHE and Unsharp Masking Suleman, Fitriyanti; Indrabayu, Indrabayu; Mokobombang, Novy Nur R.A; Zulkarnain, Eliza; Fadhil, Muh. Wira Ramdhani
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1654

Abstract

The rapid increase in vehicle numbers has rendered traditional manual traffic monitoring approaches inefficient and unreliable, thereby emphasizing the need for intelligent, automated systems to assist in traffic management and law enforcement. Among these, Automatic License Plate Recognition (ALPR) systems play a crucial role in detecting and tracking vehicles. However, their performance often deteriorates under low-light or poor visibility conditions, leading to reduced detection accuracy. To address this limitation, this study proposes a two-stage image enhancement pipeline that integrates Contrast Limited Adaptive Histogram Equalization (CLAHE) and Unsharp Masking (USM) techniques with the advanced YOLOv11 object detection model. The dataset utilized comprises 1,496 images extracted from Electronic Traffic Law Enforcement (ETLE) video footage captured in Makassar, Indonesia. These images were systematically divided into training, validation, and testing sets in a 70:20:10 ratio to ensure balanced model evaluation. Four experimental scenarios were conducted to assess the contribution of each enhancement method. The results revealed that the combined application of CLAHE and USM significantly improved detection accuracy, achieving a Precision of 0.945, Recall of 0.977, and a mean Average Precision (mAP@0.5:0.95) of 0.830—outperforming all other configurations. These findings confirm that the synergistic use of contrast enhancement and edge sharpening effectively mitigates the challenges posed by low-light environments. Consequently, the proposed approach enhances the robustness, clarity, and reliability of ALPR systems, offering a practical solution for real-world intelligent transportation applications and automated traffic law enforcement.
Co-Authors Adam, Novita Dunggio, Zufrianto Dunggio, Zufrianto K Fadhil, Muh. Wira Ramdhani Ifriandi Labolo Indrabayu Indrabayu Maryati Kadir Thalib Misrawati Aprilyana Puspa Mokobombang, Novy Nur R.A Noviyanti Tue Nur Oktavin Idris Rapika Anwar Sri Oktavia Dai Suleman, Nurjana Talib, Maryati Kadir Utiarahman, Siti Andini Yundari, Yundari Zufrianto K Dunggio Zulkarnain, Eliza