An Automatic Number Plate Recognition System for Car Park Management

Mandi, Mutua Simon; Kasamani, Bernard Shibwabo; Raphael, Kaibiru Mutua

doi:10.5120/ijca2017915608

Cited by 12 publications

(4 citation statements)

References 4 publications

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“…With a growing number of vehicles around the globe [16], an automatic and robust authentication system would be required to manage and monitor vehicles. An intelligent traffic system can help us to curb wrong-side driving, reckless driving, and vehicle authorization [17][18][19][20][21][22]. With the advancement of technology, both in hardware and software, it is now possible to make smart, automated, intelligent traffic systems.…”

Section: Literature Reviewmentioning

confidence: 99%

An Automated Precise Authentication of Vehicles for Enhancing the Visual Security Protocols

Roy,

Ahmad,

Ghani

et al. 2023

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The movement of vehicles in and out of the predefined enclosure is an important security protocol that we encounter daily. Identification of vehicles is a very important factor for security surveillance. In a smart campus concept, thousands of vehicles access the campus every day, resulting in massive carbon emissions. Automated monitoring of both aspects (pollution and security) are an essential element for an academic institution. Among the reported methods, the automated identification of number plates is the best way to streamline vehicles. The performances of most of the previously designed similar solutions suffer in the context of light exposure, stationary backgrounds, indoor area, specific driveways, etc. We propose a new hybrid single-shot object detector architecture based on the Haar cascade and MobileNet-SSD. In addition, we adopt a new optical character reader mechanism for character identification on number plates. We prove that the proposed hybrid approach is robust and works well on live object detection. The existing research focused on the prediction accuracy, which in most state-of-the-art methods (SOTA) is very similar. Thus, the precision among several use cases is also a good evaluation measure that was ignored in the existing research. It is evident that the performance of prediction systems suffers due to adverse weather conditions stated earlier. In such cases, the precision between events of detection may result in high variance that impacts the prediction of vehicles in unfavorable circumstances. The performance assessment of the proposed solution yields a precision of 98% on real-time data for Malaysian number plates, which can be generalized in the future to all sorts of vehicles around the globe.

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Section: Literature Reviewmentioning

confidence: 99%

An Automated Precise Authentication of Vehicles for Enhancing the Visual Security Protocols

Roy,

Ahmad,

Ghani

et al. 2023

Information

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show abstract

“…Another system was developed to monitor the car parking system efficiently using ANPR camera to capture the images and it provided the parking location to the owner by processing the images [5,12]. This System had given the idea of object detection and also provided the parking details to the respective owners.…”

Section: Related Workmentioning

confidence: 99%

Automatic Number Plate Recognition Using Machine Learning

Pujar¹,

Kulkarni²

2023

Advances in Computer Science Research

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Automatic number plate recognition makes use of Image processing and Machine learning technology to detect vehicle numbers irrespective of climatic conditions. As the number of vehicles is increasing day by day, it's necessary to consider safety measures. It is used in various fields like traffic control, automatic road tax collection at toll areas and vehicle parking systems in crowded areas. The goal of the proposed system is to develop a capable automatic authorized vehicle recognition system that makes use of the vehicle number plate. For detecting number plate, an infrared (IR) sensor is used, which helps in taking clear images from a camera. Taking images of moving vehicles is the most important and difficult task. Character segmentation is used to extract the vehicle number plate region from an image by using R-CNN method. The Optical Character Recognition (OCR) method is used for identifying the accurate character. The collected data is then compared with the respective authority databases to investigate specific information like vehicle owner, registration location, address and so on. If the vehicle details match those in the database, then only gate barricade is opened. Through this system, criminal activities can be minimized and road safety measures will be considered. The system tries to promote the efficiency and accuracy of number plate detection over climatic conditions.

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“…License plate recognition (LPR) technology plays a pivotal role in smart parking systems by enabling automated vehicle identification, entry/exit control, and payment processes [4,5]. LPR involves the detection, extraction, and recognition of license plate information from images or videos.…”

Section: Introductionmentioning

confidence: 99%

A Single-Stage Deep Learning-based Approach for Real-Time License Plate Recognition in Smart Parking System

YU,

LIU

2023

IJACSA

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License plate recognition in smart parking systems plays a crucial role in enhancing parking management efficiency and security. Traditional methods and deep learning-based approaches have been explored for license plate recognition. Deep learning methods have gained prominence due to their ability to extract meaningful features and achieve high accuracy rates. However, existing deep learning-based fire detection methods face challenges in terms of accuracy, real-time requirement, and computation cost, as evident from previous studies. To address these challenges, we propose a single-stage deep learning approach using YOLO (You Only Look Once) algorithm. Our method involves generating a custom dataset and conducting training, validation, and testing processes to train the YOLO-based model. Experimental results and performance evaluations demonstrate that our proposed method achieves high accuracy rates and satisfies real-time requirements, validating its effectiveness for license plate recognition in smart parking systems.

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An Automatic Number Plate Recognition System for Car Park Management

Cited by 12 publications

References 4 publications

An Automated Precise Authentication of Vehicles for Enhancing the Visual Security Protocols

An Automated Precise Authentication of Vehicles for Enhancing the Visual Security Protocols

Automatic Number Plate Recognition Using Machine Learning

A Single-Stage Deep Learning-based Approach for Real-Time License Plate Recognition in Smart Parking System

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