Preprocessing Image for License Plate Detection: A Systematic Literature Review

Riyan Bagas Dwi  Prasetyo; Vugar  Abdullayev; Nurcahya Pradana Taufik  Prakisya; Yudianto  Sujana; Rahmat  Siswanto

doi:10.69616/mcs.v2i2.241

Authors

Riyan Bagas Dwi Prasetyo Universitas Sebelas Maret
Vugar Abdullayev Azerbaijan State Oil and Industry University
Nurcahya Pradana Taufik Prakisya Universitas Sebelas Maret https://orcid.org/0000-0002-7664-7156
Yudianto Sujana Universitas Sebelas Maret
Rahmat Siswanto Universiti Malaysia Kelantan

DOI:

https://doi.org/10.69616/mcs.v2i2.241

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Keywords:

Deep Learning, License Plate Detection, Systematic Literature Review, Preprocessing

Abstract

Rapid population growth contributes to an increase in the volume of vehicles, creating major challenges in their management. One potential solution is the application of deep learning-based artificial intelligence technology for automatic detection of vehicle license plates. This research uses a Systematic Literature Review (SLR) approach to evaluate the performance of various deep learning architectures in the detection process. Out of 125 articles identified, 20 articles were selected based on specific selection criteria. The analysis revealed that preprocessing techniques, such as HE, AHE, ECHE, CLAHE, and ECLACHE, have significant contributions in the processing of vehicle license plate datasets. These techniques were able to improve the visual quality of the images, thus supporting the detection process with an accuracy rate of more than 95%. This research also identified challenges, such as high computational requirements and large-scale data processing. Further research is recommended to apply preprocessing on standardized datasets to develop a reliable, efficient and sustainable detection system.

Author Biographies

Riyan Bagas Dwi Prasetyo, Universitas Sebelas Maret

Informatics and Computer Engineering Education

Vugar Abdullayev, Azerbaijan State Oil and Industry University

Computer Engineering

Nurcahya Pradana Taufik Prakisya, Universitas Sebelas Maret

Informatics and Computer Engineering Education

Yudianto Sujana, Universitas Sebelas Maret

Informatics and Computer Engineering Education

Rahmat Siswanto, Universiti Malaysia Kelantan

Artificial Intelligence

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