Version 1
: Received: 27 September 2023 / Approved: 28 September 2023 / Online: 29 September 2023 (14:07:38 CEST)
How to cite:
Fadili, A.; El Aroussi, M.; Saadane, R.; Fakhri, Y. Fastest Moroccan License Plate Recognition Using a Lightweight Modified YOLOv5 Model. Preprints2023, 2023092107. https://doi.org/10.20944/preprints202309.2107.v1
Fadili, A.; El Aroussi, M.; Saadane, R.; Fakhri, Y. Fastest Moroccan License Plate Recognition Using a Lightweight Modified YOLOv5 Model. Preprints 2023, 2023092107. https://doi.org/10.20944/preprints202309.2107.v1
Fadili, A.; El Aroussi, M.; Saadane, R.; Fakhri, Y. Fastest Moroccan License Plate Recognition Using a Lightweight Modified YOLOv5 Model. Preprints2023, 2023092107. https://doi.org/10.20944/preprints202309.2107.v1
APA Style
Fadili, A., El Aroussi, M., Saadane, R., & Fakhri, Y. (2023). Fastest Moroccan License Plate Recognition Using a Lightweight Modified YOLOv5 Model. Preprints. https://doi.org/10.20944/preprints202309.2107.v1
Chicago/Turabian Style
Fadili, A., Rachid Saadane and Youssef Fakhri. 2023 "Fastest Moroccan License Plate Recognition Using a Lightweight Modified YOLOv5 Model" Preprints. https://doi.org/10.20944/preprints202309.2107.v1
Abstract
The rate of accidents in Morocco is experiencing a significant increase. Automatic license plate detection and recognition (ALPR) is an essential road safety technology. It facilitates applications such as traffic control, law enforcement, and toll collection by allowing for the automated recognition of vehicles on the road. In this study, we incorporated ShuffleNet V2 into the end-to-end YOLOV5 object detection system. The objective was to develop a model capable of identifying Moroccan license plates with an accuracy of 87%. The proposed model is intended to attain a high processing performance of 60 frames per second (FPS) while maintaining a low weight of 1.3 megabytes (MB) and a parameter count of 0.44 million floating point operations (MGFLOP). Our model maintains superior performance and is highly compatible with embedded systems compared to other models utilized in the same context.
Keywords
License plate detection; deep learning; YOLOv5; ShuffleNet
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
