Notice of Violation of IEEE Publication Principles: An Automated Car Plate Identification Systems based on YOLO: Techniques and Methods

Notice of Violation of IEEE Publication Principles

“An Automated Car Plate Identification Systems based on YOLO: Techniques and Methods”
by Dhurghashini Munusamy, Mohammed Hazim Alkawaz, Arshad Jamal, Asif Iqbal Hajamydeen
in the Proceedings of the IEEE 12th Control and System Graduate Research Colloquium (ICSGRC), August 2021

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles.

This paper copied content from the paper cited below. The original content was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.

“A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector”
by Rayson Laroca, Evair Severo, Luiz A. Zanlorensi, Luiz S. Oliveira, Gabriel Resende Goncalves, William Robson Schwartz, David Menotti
in the Proceedings of the International Joint Conference on Neural Networks (IJCNN), 2018


Automatic License Plate Identification (ALPR) is a progressive topic of evaluation due to numerous functional applications commonly used by companies. This paper presents the earlier methods, techniques, and algorithms for vigorous and effective ALPR frameworks dependent on the cutting-edge object finder such as the Convolutional Neural Web (CNWs) which has been prepared as a fine-tuned design of ALPR. But then this method has failed in the ability to endure (e.g., varieties in-camera, brightness, and foundation) as well as creating psyche divisions and acknowledgment. A two-period strategy used simple data augmentation techniques like flipped License Plates (LPs) and reversed psyches. In the two datums, the ALPR technique produced remarkable effects.