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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science

Usman Ullah Sheikh

Universiti Teknologi Malaysia

Author-ID : 2406436

Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 8 Documents Claim Missing Document

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Articles

Title

Recognition of vehicle make and model in low light conditions Aymen Fadhil Abbas; Usman Ullah Sheikh; Mohd Norzali Haji Mohd
Bulletin of Electrical Engineering and Informatics Vol 9, No 2: April 2020
Publisher : Institute of Advanced Engineering and Science

This paper presents a method for vehicle make and model recognition (MMR) in low lighting conditions. While many MMR methods exist in the literature, these methods are designed to be used only in perfect operating conditions. The various camera configuration, lighting condition, and viewpoints cause variations in image quality. In the presented method, the vehicle is first detected, image enhancement is then carried out on the detected front view of the vehicle, followed by features extraction and classification. The performance is then examined on a low-light dataset. The results show around 6% improvement in the ability of MMR with the use of image enhancement over the same recognition model without image enhancement.

Malaysian car plate localization using region-based convolutional neural network Tay Eng Liang; Usman Ullah Sheikh; Mohd Norzali Haji Mohd
Bulletin of Electrical Engineering and Informatics Vol 9, No 1: February 2020
Publisher : Institute of Advanced Engineering and Science

Automatic car plate localization and recognition system is a system that identifies the car plate location and recognizes the characters on the car plate input images. Within the automated system, the car plate localization stage is the first stage and is the most crucial stage as the success rate of the whole system depends heavily on it. In this paper, a Malaysian car plate localization system using Region-based Convolutional Neural Network (R-CNN) is proposed. Using transfer learning on the AlexNet CNN, the localization was greatly improved achieving best precision and recall rate of 95.19% and 97.84% respectively. Besides, the proposed R-CNN was able to localize car plates in complex scenarios such as under occlusion.

Co-Authors Ab Al-Hadi Ab Rahman Abu Khari A’ain Ainy Haziyah Awab Arbab Alamgir Attaullah Khidrani Awais Gul Airij Aymen Fadhil Abbas Farrukh Hafeez Goh Kam Meng Heh Whit Ney Izam Kamisian Lee Yoot Khuan Mohd Norzali Haji Mohd Mohd Shahrizal Rusli Muhammad Akram Bhayo Muhammad Mun'im Ahmad Zabidi Nor Aini Zakaria Norhafizah Ramli Norlina Paraman Ong Jun Ying Rubita Sudirman Saleh Masoud Abdallah Altbawi Tay Eng Liang Touqeer Ahmed Jumani

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Journal : Bulletin of Electrical Engineering and Informatics