License Plate Recognition Model based on Improved YOLOv5 and Convolutional LSTM
subject: and licence plate recognition; attention mechanism; connectionist temporal softmax; convolutional long short-term memory; deep learning
description: An end-to-end deep learning model is proposed in this research, for licence plate recognition (LPR) and identification in natural circumstances, which addresses the accuracy and speed limitations of standard licence plate recognition approaches. By adding a better channel attention mechanism and including position data in the output, the proposed method improves the You Only Look Once (YOLOv5) down sampling process and reduces information loss during sampling for better feature extraction. An optimised the YOLO layer is used for single-class recognition to improve efficiency and accuracy. Additionally, Convolutional Long Short-Term Memory (ConvLSTM) combined with Connectionist Temporal Softmax (CTS) is used for character segmentation-free recognition. The utilization of an optimized YOLO layer for single-class recognition enhances both efficiency and accuracy. The integration of ConvLSTM in conjunction with CTS proves to be a breakthrough, facilitating faster convergence, reduced training time, and increase the precision of the model. This configuration speeds up convergence, lowers training time, and increases identification accuracy. The experimental results demonstrate average recognition precision of 99.24% and also robustness, especially in complex situations, with better performance than conventional algorithms. 2025 IEEE.
creator: Alzubaidi, Laith H.; Rajendiran, M.; Sattar, Khalid Nazim Abdul; David, Leo Gertrude; Pani, Alok Kumar
source: 4th IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2025;
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISBN: 979-833153366-3;
language: English
type: Conference paper
coverage: Alzubaidi L.H., The Islamic University, Department Computer's Techniques Engineering, Najaf, Iraq; Rajendiran M., Qis College of Engineering and Technology, Department of Computer Science and Engineering, Ongole, India; Sattar K.N.A., Majmaah University, College of Science, Department of Csi, Al Majmaah, Saudi Arabia; David L.G., Kumaraguru College of Liberal Arts and Science, Department of Visual Communication, Coimbatore, India; Pani A.K., Christ (Deemed to be University), Department of Computer Science and Engineering, Bangalore, India