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All Journal Jurnal Minfo Polgan (JMP)
Afta Ramadhan Zayn
Politeknik Negeri Malang
Computer Science & IT Library & Information Science Mathematics Social Sciences

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Implementasi CLAHE dan YOLOv11 untuk Pembacaan Pelat Nomor Kendaraan Beresolusi Rendah pada Raspberry Pi 5 Benni Agung Nugroho; Afta Ramadhan Zayn; Ellya Nurfarida; Riswan Eko Wahyu Susanto; Hadi Rahmad
Jurnal Minfo Polgan Vol. 15 No. 2 (2026): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v15i2.16669

Abstract

Vehicle license plate recognition in bus fleets often faces obstacles due to small plate size, low image resolution, and poor contrast, making characters difficult to recognize. This study proposes a low-resolution bus license plate character reading system using the You Only Look Once version 11 (YOLOv11) model combined with the Contrast Limited Adaptive Histogram Equalization (CLAHE) method as a preprocessing stage in the inference process. The training dataset was developed from a combination of synthetic license plate images and low-resolution real vehicle license plate images. The model was trained using an NVIDIA A100 GPU for 500 epochs and implemented on a Raspberry Pi 5 as an edge device. Validation results show the model achieved a precision of 88.9%, a recall of 76.2%, an mAP@50 of 87.2%, and an mAP@50–95 of 53.4%. Testing on real vehicle license plate images shows that the application of CLAHE increases the success rate of character reading from 82.13% to 91.04% with a relatively small additional inference time. Furthermore, implementation on a Raspberry Pi 5 demonstrates that the model can be run efficiently with low resource usage, making it worthy of consideration as an Artificial Intelligence of Things (AIoT)-based Automatic Number Plate Recognition (ANPR) solution.
Co-Authors Benni Agung Nugroho Ellya Nurfarida Hadi Rahmad Riswan Eko Wahyu Susanto