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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science
Dilshad Islam
Chattogram Veterinary and Animal Sciences University
Computer Science & IT

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An efficient automated vehicle license plate recognition system under image processing Dilshad Islam; Tanjim Mahmud; Tanjia Chowdhury
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp1055-1062

Abstract

An automated vehicle license plate recognition system using image processing techniques identifies vehicle numbers without human interference. This system has significant impact because of its good application in various fields like car parking, access control, speed control, automatic toll collection, border security, traffic violence detection and surveillance applications. This paper presents a methodology that is quite simple but at the same time very much efficient and this system consists of four sequential modules which are preprocessing, number plate extraction, number plate character segmentation and character recognition. Preprocessing aims to improve the image quality that is captured in various illumination conditions and stick out outstanding information that we need, which is favorable to subsequent processing including extraction, segmentation and recognition. After preprocessing various morphological operations are applied to extract the desired license plate region. Then for segmentation the bounding box method is applied that segments each letter and number present on the license plate region. Finally, template matching is applied in identifying all segmented characters present in the license plate image. The experimental results showed that the proposed system can recognize license plate characters efficiently with higher accuracy. Using MATLAB software, the proposed method attains recognition accuracy of 94.17%.
Convolutional neural network-based real-time drowsy driver detection for accident prevention Nippon Datta; Tanjim Mahmud; Manoara Begum; Mohammad Tarek Aziz; Dilshad Islam; Md. Faisal Bin Abdul Aziz; Khudaybergen Kochkarov; Temur Eshchanov; Valisher Sapayev Odilbek Uglu; Sobir Parmanov; Mohammad Shahadat Hossain; Karl Andersson
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 3: June 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i3.26059

Abstract

Drowsy driving significantly threatens road safety, contributing to many accidents globally. This paper presents a convolutional neural network (CNN)-based real-time drowsy driver detection system aimed at preventing such accidents, particularly for deployment in Android applications. We propose a lightweight CNN architecture that effectively identifies drowsiness and microsleep episodes by categorizing driver facial expressions into four distinct categories: close-eye expressions, open-eye expressions, yawns, and no yawns. Our model, which employs facial landmark detection and various pre-processing techniques to enhance accuracy, achieves an impressive 96.6% accuracy. This performance surpasses several popular CNN architectures, including VGG16, VGG19, MobileNetV2, ResNet50, and DenseNet121. Notably, our proposed model is highly efficient, with only 0.4 million parameters and a memory requirement of 1.51 MB, making it ideal for real-time applications. The comparative analysis highlights the superior balance between accuracy and resource efficiency of our model, demonstrating its potential for practical deployment in reducing accidents caused by driver fatigue.
Co-Authors Karl Andersson Khudaybergen Kochkarov Manoara Begum Md. Faisal Bin Abdul Aziz Mohammad Shahadat Hossain Mohammad Tarek Aziz Nippon Datta Sobir Parmanov Tanjia Chowdhury Tanjim Mahmud Tanjim Mahmud Temur Eshchanov Valisher Sapayev Odilbek Uglu