Vehicle License Plate Recognition With Deep Learning

2022 ◽  
pp. 161-219
Author(s):  
Chi-Hsuan Huang ◽  
Yu Sun ◽  
Chiou-Shana Fuh
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Neural Network Model ◽  
Power Supply ◽  
High Speed ◽  
License Plate ◽  
License Plate Recognition ◽  
Moving Vehicles

In this chapter, an AI (artificial intelligence) solution for LPR (license plate recognition) on moving vehicles is proposed. The license plates in images captured with cameras on moving vehicles have unpredictable distortion and various illumination which make traditional machine vision algorithms unable to recognize the numbers correctly. Therefore, deep learning is leveraged to recognize license plate in such challenging conditions for better recognition accuracy. Additionally, lightweight neural networks are chosen since the power supply of scooter is quite limited. A two-stage method is presented to recognize license plate. First, the license plates in captured images are detected using CNN (convolutional neural network) model and the rotation of the detected license plates are corrected. Subsequently, the characters are recognized as upper-case format (A-Z) and digits (0-9) with second CNN model. Experimental results show that the system achieves 95.7% precision and 95% recall at high speed during the daytime.

Comprehensive Overview of Neural Networks and Its Applications in Autonomous Vehicles

2019 ◽  
pp. 159-173
Author(s):  
Jay Rodge ◽  
Swati Jaiswal
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Learning Algorithms ◽  
Activation Functions ◽  
Comprehensive Overview ◽  
Unit Component

Deep learning and Artificial intelligence (AI) have been trending these days due to the capability and state-of-the-art results that they provide. They have replaced some highly skilled professionals with neural network-powered AI, also known as deep learning algorithms. Deep learning majorly works on neural networks. This chapter discusses about the working of a neuron, which is a unit component of neural network. There are numerous techniques that can be incorporated while designing a neural network, such as activation functions, training, etc. to improve its features, which will be explained in detail. It has some challenges such as overfitting, which are difficult to neglect but can be overcome using proper techniques and steps that have been discussed. The chapter will help the academician, researchers, and practitioners to further investigate the associated area of deep learning and its applications in the autonomous vehicle industry.

scholarly journals Deep learning Convolutional Neural Network for Unconstrained License Plate Recognition

2019 ◽  
Vol 255 ◽  
pp. 05002
Author(s):  
Pang Yee Yong ◽  
Ong Chee Hau ◽  
Sim Hiew Moi
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Traffic Monitoring ◽  
Transportation Systems ◽  
License Plate ◽  
License Plate Recognition ◽  
Statistical Research ◽  
Automatic Vehicle Identification ◽  
Vehicle Information

The evolve of neural networks algorithm into deep learning convolutional neural networks seems like the next generation for object detection. This algorithm works has a significantly better accuracy and did not tied to any particular aspect ratio. License plate and traffic signs detection and recognition have a number of different applications relevant for transportation systems, such as traffic monitoring, detection of stolen vehicles, driver navigation support or any statistical research. An exponential increase in number of vehicles necessitates the use of automated systems to maintain vehicle information. The information is highly required for both management of traffic as well as reduction of crime. Number plate recognition is an effective way for automatic vehicle identification. A number of methods have been proposed, but only for particular cases and working under constraints (e.g. known text direction or high resolution). Deep learning convolutional neural networks work well especially in handles occlusion/rotation better, therefore we believe this approach is able to provide a better solution to the unconstrained license plate recognition problem.

License Plate Recognition Based on Neural Network Algorithm to Improve Research

2013 ◽  
Vol 860-863 ◽  
pp. 2892-2897 ◽  
Author(s):  
De Yong Liu ◽  
Hong Song ◽  
Quan Pan
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Bp Neural Network ◽  
High Speed ◽  
Recognition Rate ◽  
Training Transfer ◽  
License Plate ◽  
License Plate Recognition ◽  
The Real ◽  
Time Performance

with the development of intelligent transportation technology, which all countries are suitable for their own license plate recognition system is developed. But because of the CCD camera Angle problem will make license plate image tilt; Segmentation after do not match the characters in size and character discontinuity, led to license plate recognition rate is not high, speed slow, reduce the real-time performance of the system. In order to improve the rate of convergence, the recognition rate presents a license plate recognition algorithm based on BP neural network. First put the image correction, segmentation of character normalization processing and eliminate the unfavorable factors, then puts forward characteristics of characters input for training the BP neural network. By setting the network weights and training transfer function, improved algorithm to improve the recognition rate of the system, as well as the real-time performance.

scholarly journals Optical coherent dot-product chip for sophisticated deep learning regression

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Shaofu Xu ◽  
Jing Wang ◽  
Haowen Shu ◽  
Zhike Zhang ◽  
Sicheng Yi ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Language Processing ◽  
High Speed ◽  
Autonomous Driving ◽  
Single Chip ◽  
Optical Fields ◽  
Large Bandwidth ◽  
Dot Product

AbstractOptical implementations of neural networks (ONNs) herald the next-generation high-speed and energy-efficient deep learning computing by harnessing the technical advantages of large bandwidth and high parallelism of optics. However, due to the problems of the incomplete numerical domain, limited hardware scale, or inadequate numerical accuracy, the majority of existing ONNs were studied for basic classification tasks. Given that regression is a fundamental form of deep learning and accounts for a large part of current artificial intelligence applications, it is necessary to master deep learning regression for further development and deployment of ONNs. Here, we demonstrate a silicon-based optical coherent dot-product chip (OCDC) capable of completing deep learning regression tasks. The OCDC adopts optical fields to carry out operations in the complete real-value domain instead of in only the positive domain. Via reusing, a single chip conducts matrix multiplications and convolutions in neural networks of any complexity. Also, hardware deviations are compensated via in-situ backpropagation control provided the simplicity of chip architecture. Therefore, the OCDC meets the requirements for sophisticated regression tasks and we successfully demonstrate a representative neural network, the AUTOMAP (a cutting-edge neural network model for image reconstruction). The quality of reconstructed images by the OCDC and a 32-bit digital computer is comparable. To the best of our knowledge, there is no precedent of performing such state-of-the-art regression tasks on ONN chips. It is anticipated that the OCDC can promote the novel accomplishment of ONNs in modern AI applications including autonomous driving, natural language processing, and scientific study.

scholarly journals A Practical Tutorial on Graph Neural Networks

2022 ◽  
Author(s):  
Isaac Ronald Ward ◽  
Jack Joyner ◽  
Casey Lickfold ◽  
Yulan Guo ◽  
Mohammed Bennamoun
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Input Data ◽  
Unstructured Data ◽  
Data Types ◽  
Learning Techniques ◽  
Graph Neural Networks ◽  
Unique Ability

Graph neural networks (GNNs) have recently grown in popularity in the field of artificial intelligence (AI) due to their unique ability to ingest relatively unstructured data types as input data. Although some elements of the GNN architecture are conceptually similar in operation to traditional neural networks (and neural network variants), other elements represent a departure from traditional deep learning techniques. This tutorial exposes the power and novelty of GNNs to AI practitioners by collating and presenting details regarding the motivations, concepts, mathematics, and applications of the most common and performant variants of GNNs. Importantly, we present this tutorial concisely, alongside practical examples, thus providing a practical and accessible tutorial on the topic of GNNs.

RECOGNITION OF THE TEXT BY MEANS OF DEEP LEARNING

2020 ◽  
Vol 69 (1) ◽  
pp. 378-383
Author(s):  
T.A. Nurmukhanov ◽  
◽  
B.S. Daribayev ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Neural Network Model ◽  
Input Data ◽  
Optimization Methods ◽  
Text Recognition ◽  
Activation Functions

Using neural networks, various variations of the classification of objects can be performed. Neural networks are used in many areas of recognition. A big area in this area is text recognition. The paper considers the optimal way to build a network for text recognition, the use of optimal methods for activation functions, and optimizers. Also, the article checked the correctness of text recognition with different optimization methods. This article is devoted to the analysis of convolutional neural networks. In the article, a convolutional neural network model will be trained with a teacher. Teaching with a teacher is a type of training for neural networks in which you provide the input data and the desired result, that is, the student looking at the input data will understand that you need to strive for the result that was provided to him.

scholarly journals MoView Engine : An Open Source Movie Recommender

2020 ◽  
Vol 32 ◽  
pp. 03008
Author(s):  
Vallari Manavi ◽  
Anjali Diwate ◽  
Priyanka Korade ◽  
Anita Senathi
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Human Brain ◽  
Network Model ◽  
Neural Network Model ◽  
Recommendation System ◽  
Activation Function ◽  
End Users ◽  
History Of

Recommendation is an ideology that works as choice-based system for the end users. Users are recommended with their favorite movies based on history of other watched movies or based on the category of the movies. These types of recommendations are becoming popular because of their ability to think and react as human brain. For this purpose, deep learning or artificial intelligence comes into picture. It is the ability to think as a human brain as give the output best suited to the end users liking. This paper focuses on implementing the recommendation system of movies using deep learning with neural network model using the activation function of SoftMax to give an experience to users as friendly recommendation. Moreover, this paper focuses on different scenarios of recommendation like the recommendation based on history, genre of the movie etc.

scholarly journals Neural Reversible Steganography with Long Short-Term Memory

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Ching-Chun Chang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Low Level ◽  
Long Short Term Memory

Deep learning has brought about a phenomenal paradigm shift in digital steganography. However, there is as yet no consensus on the use of deep neural networks in reversible steganography, a class of steganographic methods that permits the distortion caused by message embedding to be removed. The underdevelopment of the field of reversible steganography with deep learning can be attributed to the perception that perfect reversal of steganographic distortion seems scarcely achievable, due to the lack of transparency and interpretability of neural networks. Rather than employing neural networks in the coding module of a reversible steganographic scheme, we instead apply them to an analytics module that exploits data redundancy to maximise steganographic capacity. State-of-the-art reversible steganographic schemes for digital images are based primarily on a histogram-shifting method in which the analytics module is often modelled as a pixel intensity predictor. In this paper, we propose to refine the prior estimation from a conventional linear predictor through a neural network model. The refinement can be to some extent viewed as a low-level vision task (e.g., noise reduction and super-resolution imaging). In this way, we explore a leading-edge neuroscience-inspired low-level vision model based on long short-term memory with a brief discussion of its biological plausibility. Experimental results demonstrated a significant boost contributed by the neural network model in terms of prediction accuracy and steganographic rate-distortion performance.

scholarly journals Pop-In Identification in Nanoindentation Curves with Deep Learning Algorithms

Materials ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7027
Author(s):  
Stephania Kossman ◽  
Maxence Bigerelle
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
High Speed ◽  
Large Datasets ◽  
Loading Path ◽  
Load Displacement ◽  
Deep Learning Model ◽  
Data Analysis Methods

High–speed nanoindentation rapidly generates large datasets, opening the door for advanced data analysis methods such as the resources available in artificial intelligence. The present study addresses the problem of differentiating load–displacement curves presenting pop-in, slope changes, or instabilities from curves exhibiting a typical loading path in large nanoindentation datasets. Classification of the curves was achieved with a deep learning model, specifically, a convolutional neural network (CNN) model implemented in Python using TensorFlow and Keras libraries. Load–displacement curves (with pop-in and without pop-in) from various materials were input to train and validate the model. The curves were converted into square matrices (50 × 50) and then used as inputs for the CNN model. The model successfully differentiated between pop-in and non-pop-in curves with approximately 93% accuracy in the training and validation datasets, indicating that the risk of overfitting the model was negligible. These results confirmed that artificial intelligence and computer vision models represent a powerful tool for analyzing nanoindentation data.

scholarly journals Generative Art: Between the Nodes of Neuron Networks

Artnodes ◽  
2020 ◽  
Author(s):  
Bruno Caldas Vianna
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Generative Art ◽  
Creation Process ◽  
Neuron Networks ◽  
Starting Point ◽  
Deep Learning Neural Network ◽  
In The Beginning

This article uses the exhibition “Infinite Skulls”, which happened in Paris in the beginning of 2019, as a starting point to discuss art created by artificial intelligence and, by extension, unique pieces of art generated by algorithms. We detail the development of DCGAN, the deep learning neural network used in the show, from its cybernetics origin. The show and its creation process are described, identifying elements of creativity and technique, as well as question of the authorship of works. Then it frames these works in the context of generative art, pointing affinities and differences, and the issues of representing through procedures and abstractions. It describes the major breakthrough of neural network for technical images as the ability to represent categories through an abstraction, rather than images themselves. Finally, it tries to understand neural networks more as a tool for artists than an autonomous art creator.

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