Identification of Vehicle Logos in Deep Learning: A Comprehensive Survey

Mustafa Noaman Kadhim; Ammar Hussein Mutlag; Dalal Abdulmohsin Hammood; Nurulisma Binti Hj. Ismail

doi:10.51173/jt.v7i1.2099

Authors

Mustafa Noaman Kadhim Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq
Ammar Hussein Mutlag Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq
Dalal Abdulmohsin Hammood Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq
Nurulisma Binti Hj. Ismail Faculty of Electronic Engineering & Technology (FKTEN), Universiti Malaysia Perlis (UniMAP) 02600 Arau, Perlis, Malaysia

DOI:

https://doi.org/10.51173/jt.v7i1.2099

Keywords:

Vehicle Logo Detection, Deep Learning, Small Object Detection, Convolutional Neural Networks, Yolo

Abstract

The identification of vehicle logos in videos and images can be considered a crucial undertaking in several applications, such as traffic surveillance systems. The accelerated progress of deep learning has resulted in an increasing need within the computer vision field for the development of efficient, robust, and outstanding services across several domains, such as the recognition and classification of automobile emblems. This survey begins with an exploration of the escalating significance of logos and the associated challenges to their detection. The core problem addressed revolves around the necessity for robust methodologies capable of accurately identifying logos in diverse scenarios. The objective of our study is to conduct a comprehensive examination of existing deep learning strategies for logo detection, unveil their real-world applications, and contribute insights into future challenges and directions in this domain. Our survey uncovers valuable insights into publicly available datasets, showcasing their diversity and relevance in evaluating logo detection algorithms. An in-depth analysis of deep learning strategies follows, elucidating their strengths and limitations and providing a nuanced understanding of their performance metrics. The survey concludes by delineating anticipated challenges and proposing future directions, thereby presenting a roadmap for researchers and practitioners seeking to advance logo detection using deep learning techniques.

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Author Biographies

Mustafa Noaman Kadhim, Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq

Department of Computer Technical Engineering

Ammar Hussein Mutlag, Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq

Ammar Hussein Mutlag (Member, IEEE) received the B.Sc. and M.Sc. degrees in control and computer engineering from the University of Technology, Iraq, in 2000 and 2005, respectively, and the Ph.D. degree in control and computer engineering from Universiti Kebangsaan Malaysia (UKM), Malaysia, in 2016. He is currently the Vice Dean of scientific and students affairs with the Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq, as an Assistant Professor. His research interests include intelligent controllers, microcontroller applications, developed optimization algorithms, intelligent controllers-based authentication, and intelligent decision-support systems.

Dalal Abdulmohsin Hammood, Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq

Dalal Abdulmohsin Hammood received the B.Sc. degree in computer engineering and data technology from the University of Technology, Baghdad, in 2003, and the M.Sc. degree in computers engineering techniques from the Electrical Engineering Technical College, Middle Technical University, in 2011. She is currently pursuing the Ph.D. degree with the School of Computer and Communication Engineering, Universiti Malaysia Perlis, Perlis, Malaysia. She contributed in organizing several IEEE conferences in Malaysia in 2010 and 2011, in Egypt, Jordan, and Ukraine, in 2010, 2013, and 2016, respectively. Her research interests include computer networks security, cryptography, artificial intelligence—artificial neural networks, genetic algorithms, wireless sensor networks, and swireless body area networks.

Nurulisma Binti Hj. Ismail, Faculty of Electronic Engineering & Technology (FKTEN), Universiti Malaysia Perlis (UniMAP) 02600 Arau, Perlis, Malaysia

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