An Approach of Image Enhancement Technique in Recognizing the Number Plate Location

Kranthi, S.; Pranathi, K.; Srisaila, A.

doi:10.1109/icisc44355.2019.9036434

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…Secondly, stages of image processing must be identified [6] whatever to use preprocessing or not, how exactly processing of the image will look like, what about post-processing and so on. All these questions are part of main method selection.…”

Section: Fig 2 Camera Position Invariancementioning

confidence: 99%

Usage of Mask R-CNN for Automatic License Plate Recognition

Podorozhniak

Liubchenko²,

Sobol³

et al. 2023

A.I.S.

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The subject of study is the creation process of an artificial intelligence system for automatic license plate detection. The goal is to achieve high license plate recognition accuracy on large camera angles with character extraction. The tasks are to study existing license plate recognition technics and to create an artificial intelligence system that works on big shooting camera angles with the help of modern machine learning solution – deep learning. As part of the research, both hardware and software-based solutions were studied and developed. For testing purposes, different datasets and competing systems were used. Main research methods are experiment, literature analysis and case study for hardware systems. As a result of analysis of modern methods, Mask R-CNN algorithm was chosen due to high accuracy. Conclusions. Problem statement was declared; solution methods were listed and characterized; main algorithm was chosen and mathematical background was presented. As part of the development procedure, accurate automatic license plate system was presented and implemented in different hardware environments. Comparison of the network with existing competitive systems was made. Different object detection characteristics, such as Recall, Precision and F1-Score, were calculated. The acquired results show that developed system on Mask R-CNN algorithm process images with high accuracy on large camera shooting angles.

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Section: Fig 2 Camera Position Invariancementioning

confidence: 99%

Usage of Mask R-CNN for Automatic License Plate Recognition

Podorozhniak

Liubchenko²,

Sobol³

et al. 2023

A.I.S.

View full text Add to dashboard Cite

show abstract

“…One of the goals of the work is to make the system efficient considering large camera shooting angles. Such restrictions complicate the use of modern ALPR systems in real situations on public roads, especially in the presence of multi-lane traffic, large camera shooting angles, bad visibility of license plates, and in difficult weather conditions [5,6].…”

Section: Fig 1 -Concept Of Alpr Dronementioning

confidence: 99%

License Plate Recognition System Based on Mask R-CNN

Onishchenko,

Liubchenko,

Podorozhniak

2023

ATBP

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Automatic license plate recognition (ALPR) systems can be found in many different applications. If a person has a driving license, the person probably had already seen smart road camera after a speed limit sign or on a crossroad. Every year number of cars on roads growth very fast. It is also obvious that such systems can be used out-of-road situations. For instance, this type of systems can be used for automatic access control on private property or smart parking, or even log system that are being used literally everywhere. Because of popularity of ALPR systems, there are two main goals, which are being pursued by researches: speed and accuracy of recognition. Speed of the detection is important for real-time systems. Accuracy is important for every system. The more accurate a system is, the more reliable it is. For example, car accident detection systems should be as accurate as possible in order to be used, because no one wants to get billed with the wrongdoing, that wasn’t committed by them. The purpose of the study is to develop high precision automatic license plate detection system with number extraction possibilities. In order to achieve the goal many different modern solutions and technologies were studied and solution is presented. The main technology of the project is artificial intelligence system and, more specifically, convolutional neural network. As the main algorithm Mask R-CNN is used for license plate detection. To present reasonable research, the system was tested on different hardware (CPU, GPU, Raspberry PI 4) and on different datasets.

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