Automatic License Plate Recognition for Parking System using Convolutional Neural Networks

Joshua,; Hendryli, Janson; Herwindiati, Dyah Erny

doi:10.1109/icimtech50083.2020.9211173

Cited by 15 publications

(9 citation statements)

References 8 publications

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“…Another approach is to use the YOLO model to find the license plate region and the ResNet model to recognize the characters. Joshua et al [10] presented such a method for identifying vehicle license plates for a parking system with around 80% accuracy for character recognition on Indonesian license plates. A solution for live detection and recognition of a moving vehicle's license plate number using computer vision techniques was proposed by Darapaneni et al [11] with the best performance in terms of accuracy and speed using YOLOv3 with OpenCV.…”

Section: Related Workmentioning

confidence: 99%

Efficient Multistage License Plate Detection and Recognition Using YOLOv8 and CNN for Smart Parking Systems

Safran,

Alajmi,

Alfarhood

2024

Journal of Sensors

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Smart parking systems play a vital role in enhancing the efficiency and sustainability of smart cities. However, most existing systems depend on sensors to monitor the occupancy of parking spaces, which entail high installation and maintenance costs and limited functionality in tracking vehicle movement within the car park. To address these challenges, we propose a multistage learning-based approach that leverages existing surveillance cameras within the car park and a self-collected dataset of Saudi license plates. The approach combines YOLOv5 for license plate detection, YOLOv8 for character detection, and a new convolutional neural network architecture for improved character recognition. We show that our approach outperforms the single-stage approach, achieving an overall accuracy of 96.1% versus 83.9% of the single-stage approach. The approach is also integrated into a web-based dashboard for real-time visualization and statistical analysis of car park occupancy and vehicle movement with an acceptable time efficiency. Our work demonstrates how existing technology can be leveraged to improve the efficiency and sustainability of smart cities.

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Section: Related Workmentioning

confidence: 99%

Efficient Multistage License Plate Detection and Recognition Using YOLOv8 and CNN for Smart Parking Systems

Safran,

Alajmi,

Alfarhood

2024

Journal of Sensors

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“…There have been several compelling projects-both in academia and industry-involving vehicle number plate recognition in different domains [1,2]. This section focuses on existing work on the ANPR technology using machine learning [5,6,[9][10][11][12] and computer vision approaches [3,4,7,8,17].…”

Section: Related Workmentioning

confidence: 99%

“…The state-of-the-art approaches for ANPR utilize Machine Learning (ML) models (e.g., Convolutional Neural Networks (CNN) [9], Recurrent Neural Networks (RNN) [10], Bi-Directional Long Short Term Memory (BLSTM) [11] and Support Vector Machine (SVM) [12]) to recognize the text on license plates. However, these ML models require large and robust datasets of plate photos of different sizes, orientations, and shapes across different regions worldwide to train them.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Car Towing Management System Using ML-Powered Automatic Number Plate Recognition

Ahmed

2021

Algorithms

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Automatic Number Plate Recognition (ANPR) has been widely used in different domains, such as car park management, traffic management, tolling, and intelligent transport systems. Despite this technology’s importance, the existing ANPR approaches suffer from the accurate identification of number plats due to its different size, orientation, and shapes across different regions worldwide. In this paper, we are studying these challenges by implementing a case study for smart car towing management using Machine Learning (ML) models. The developed mobile-based system uses different approaches and techniques to enhance the accuracy of recognizing number plates in real-time. First, we developed an algorithm to accurately detect the number plate’s location on the car body. Then, the bounding box of the plat is extracted and converted into a grayscale image. Second, we applied a series of filters to detect the alphanumeric characters’ contours within the grayscale image. Third, the detected the alphanumeric characters’ contours are fed into a K-Nearest Neighbors (KNN) model to detect the actual number plat. Our model achieves an overall classification accuracy of 95% in recognizing number plates across different regions worldwide. The user interface is developed as an Android mobile app, allowing law-enforcement personnel to capture a photo of the towed car, which is then recorded in the car towing management system automatically in real-time. The app also allows owners to search for their cars, check the case status, and pay fines. Finally, we evaluated our system using various performance metrics such as classification accuracy, processing time, etc. We found that our model outperforms some state-of-the-art ANPR approaches in terms of the overall processing time.

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“…Artificial intelligence and deep learning with the research of related technologies have been rapidly growing. Image recognition has not only appeared in industry but also in daily life, such as the automatic license plate identification system in a parking garage [ 1 ], and the maturity of fruits and vegetables [ 2 ], etc. In the fashion industry, image recognition can assist in clothing design, clothing accessories collocation and related data analysis and integration [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Clothing Style Recognition Using YOLOv5

Chang

Zhang

2022

Micromachines

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With the rapid development of artificial intelligence, much more attention has been paid to deep learning. However, as the complexity of learning algorithms increases, the needs of computation power of hardware facilities become more crucial. Instead of the focus being on computing devices like GPU computers, a lightweight learning algorithm could be the answer for this problem. Cross-domain applications of deep learning have attracted great interest amongst researchers in academia and industries. For beginners who do not have enough support with software and hardware, an open-source development environment is very helpful. In this paper, a relatively lightweight algorithm YOLOv5s is addressed, and the Google Colab is used for model training and testing. Based on the developed environment, many state-of-art learning algorithms can be studied for performance comparisons. To highlight the benefits of one-stage object detection algorithms, the recognition of clothing styles is investigated. The image samples are selected from datasets of fashion clothes and the web crawling of online stores. The image data are categorized into five groups: plaid; plain; block; horizontal; and vertical. Average precison, mean average precison, recall, F1-score, model size, and frame per second are the metrics used for performance validations. From the experimental outcomes, it shows that YOLOv5s is better than other learning algorithms in the recognition accuracy and detection speed.

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Automatic License Plate Recognition for Parking System using Convolutional Neural Networks

Cited by 15 publications

References 8 publications

Efficient Multistage License Plate Detection and Recognition Using YOLOv8 and CNN for Smart Parking Systems

Efficient Multistage License Plate Detection and Recognition Using YOLOv8 and CNN for Smart Parking Systems

A Real-Time Car Towing Management System Using ML-Powered Automatic Number Plate Recognition

Deep Learning for Clothing Style Recognition Using YOLOv5

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