Design and Matlab Simulation of Persian License Plate Recognition Using Neural Network and Image Filtering for Intelligent Transportation Systems

Akbarzadeh, Omid; Khosravi, Mohammad R.; Alex, Linss T.

doi:10.52810/tpris.2021.100098

Cited by 13 publications

(7 citation statements)

References 21 publications

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“…The task of LPR has seen a rapid development, with the development and evolution of deep learning in recent years. Under normal conditions, many LPR methods have basically approached the human level [2]. However, the difficult problem of LPR is the recognition of some license plates in low light, as shown in figure 1.…”

Section: Introductionmentioning

confidence: 99%

The Low-light License Plate Recognition via CNN

Zheng¹,

Guan²,

Li³

2023

J. Phys.: Conf. Ser.

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The low-light license plate recognition (LPR) is an important task in LPR, and the task of low-light LPR is a challenge in LPR. Compared with ordinary LPR, low-light LPR is more challenging. The first is that there are few studies on low-light LPR, and there is a lack of dedicated datasets. Besides, there are few lightweight networks dedicated to low-light LPR. The lack of lightweight private networks makes it difficult to deploy LPR methods efficiently. Based on this, this paper proposes a low-light LPR method. Specifically, we propose a dataset dedicated to low-light LPR with a sample size of 19,121. Besides, we propose a lightweight LPR method implemented using a lightweight architecture similar to VGG. We also compare with some commonly used lightweight methods, and the comparison results show that our proposed method has better performance. In addition, we also visualized some data that are difficult for the model to identify, and found several types of challenging data. This result is helpful for subsequent researchers to improve on this type of data.

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Section: Introductionmentioning

confidence: 99%

The Low-light License Plate Recognition via CNN

Zheng¹,

Guan²,

Li³

2023

J. Phys.: Conf. Ser.

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“…As stated by [7], remote-controlled robots will soon become more widespread in various fields. ML, which utilizes historical data as input and can predict the future based on the output [8], is a discipline that handles a wide range of problems in several fields [9]. The importance of ML may be observed in several areas of health care, including genomic medicine [10], cancer detection [11], and early diabetes diagnosis [12].…”

Section: Introductionmentioning

confidence: 99%

Predicting the total Unified Parkinson’s Disease Rating Scale (UPDRS) based on ML techniques and cloud-based update

et al. 2023

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Nowadays, smart health technologies are used in different life and environmental areas, such as smart life, healthcare, cognitive smart cities, and social systems. Intelligent, reliable, and ubiquitous healthcare systems are a part of the modern developing technology that should be more seriously considered. Data collection through different ways, such as the Internet of things (IoT)-assisted sensors, enables physicians to predict, prevent and treat diseases. Machine Learning (ML) algorithms may lead to higher accuracy in medical diagnosis/prognosis based on health data provided by the sensors to help physicians in tracking symptom significance and treatment steps. In this study, we applied four ML methods to the data on Parkinson’s disease to assess the methods’ performance and identify the essential features that may be used to predict the total Unified Parkinson’s disease Rating Scale (UPDRS). Since accessibility and high-performance decision-making are so vital for updating physicians and supporting IoT nodes (e.g., wearable sensors), all the data is stored, updated as rule-based, and protected in the cloud. Moreover, by assigning more computational equipment and memory in use, cloud computing makes it possible to reduce the time complexity of the training phase of ML algorithms in the cases we want to create a complete structure of cloud/edge architecture. In this situation, it is possible to investigate the approaches with varying iterations without concern for system configuration, temporal complexity, and real-time performance. Analyzing the coefficient of determination and Mean Square Error (MSE) reveals that the outcomes of the applied methods are mostly at an acceptable performance level. Moreover, the algorithm’s estimated weight indicates that Motor UPDRS is the most significant predictor of Total UPDRS.

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“…Machine learning's [ 2 ] advantage is that it can automatically analyze and process unknown data by mining and learning from existing data, reducing the need for human resources to the greatest extent possible. Scholars in the United States and abroad have conducted extensive and in-depth research on object recognition, object following, navigation, monitoring, and other technologies based on robot vision [ 3 ]. Machine vision's primary goal is to enable computers to mimic human thinking activities, obtain information from visual sensors to gain an understanding of the environment, and finally realize the computer's autonomous adaptation to the environment [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Visual Object Tracking Algorithm Based on Biological Visual Information Features and Few-Shot Learning

Zhang

Yang

2022

Computational Intelligence and Neuroscience

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Eye tracking is currently a research hotspot in the territory of service robotics. There is an urgent need for machine vision technique in the territory of video surveillance, and biological visual object following is one of the important basic research problems. By tracking the object of interest and recording the tracking trajectory, we can extract a structure from a video. It can also analyze the abnormal behavior of groups or individuals in the video or assist the public security organs in inquiring and searching for evidence of criminal suspects, etc. Moving object following has always been one of the frontier topics in the territory of machine vision, and it has very important appliances in mobile robot positioning and navigation, multirobot formation, lunar exploration, and intelligent monitoring. Moving object following has always been one of the frontier topics in the territory of machine vision, and it has very important appliances in mobile robot positioning and navigation, multirobot formation, lunar exploration, and intelligent monitoring. Moving object following in visual surveillance is easily affected by factors such as occlusion, rapid object movement, and appearance changes, and it is difficult to solve these problems effectively with single-layer features. This paper adopts a visual object following algorithm based on visual information features and few-shot learning, which effectively improves the accuracy and robustness of tracking.

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Design and Matlab Simulation of Persian License Plate Recognition Using Neural Network and Image Filtering for Intelligent Transportation Systems

Cited by 13 publications

References 21 publications

The Low-light License Plate Recognition via CNN

The Low-light License Plate Recognition via CNN

Predicting the total Unified Parkinson’s Disease Rating Scale (UPDRS) based on ML techniques and cloud-based update

Visual Object Tracking Algorithm Based on Biological Visual Information Features and Few-Shot Learning

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