Automatic License Plate Recognition Technique using Convolutional Neural Network

Das, Surajit; Mukherjee, Joydeep

doi:10.5120/ijca2017914723

Cited by 7 publications

(5 citation statements)

References 6 publications

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“…We can also observe that the proposed detection models, especially higher-tier models show performance close to the current state-of-the-art server-grade models such as RPNet, although our models are designed specifically for low resources. At the same time, all models except the lower-tier ones show superior performance to Yolo-V3 [46], which is a popular general-purpose object detector that has been used in several license plate detection studies [44,45]. Meanwhile, the same trends can be observed for the recognition models as well.…”

Section: Model Performancesupporting

confidence: 55%

“…In deep learning, the problem of automatic license plate recognition was considered to be a general object detection and a character recognition problem. Therefore, some researchers [44,45] used generic object detection models such as YOLO [46] to detect the license plate. However, these methods were more robust to noise, illuminations and inclinations of the plates thus eliminating most of the limitations in the classical methods.…”

Section: Overview Of Lp Recognition Approachesmentioning

confidence: 99%

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Automated License Plate Recognition for Resource-Constrained Environments

Padmasiri

Shashirangana

Meedeniya

et al. 2022

Sensors

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The incorporation of deep-learning techniques in embedded systems has enhanced the capabilities of edge computing to a great extent. However, most of these solutions rely on high-end hardware and often require a high processing capacity, which cannot be achieved with resource-constrained edge computing. This study presents a novel approach and a proof of concept for a hardware-efficient automated license plate recognition system for a constrained environment with limited resources. The proposed solution is purely implemented for low-resource edge devices and performed well for extreme illumination changes such as day and nighttime. The generalisability of the proposed models has been achieved using a novel set of neural networks for different hardware configurations based on the computational capabilities and low cost. The accuracy, energy efficiency, communication, and computational latency of the proposed models are validated using different license plate datasets in the daytime and nighttime and in real time. Meanwhile, the results obtained from the proposed study have shown competitive performance to the state-of-the-art server-grade hardware solutions as well.

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Section: Model Performancesupporting

confidence: 55%

Section: Overview Of Lp Recognition Approachesmentioning

confidence: 99%

Automated License Plate Recognition for Resource-Constrained Environments

Padmasiri

Shashirangana

Meedeniya

et al. 2022

Sensors

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show abstract

“…The major function of the fully connected layer is to classify the input image into a variety of classes. The Softmax function is used in its output layer [44].…”

Section: F Fully Connected Layermentioning

confidence: 99%

Micro Expression Recognition: Multi-scale Approach to Automatic Emotion Recognition by using Spatial Pyramid Pooling Module

Sian¹,

Stofa²,

Min³

et al. 2021

IJACSA

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Facial expression is one of the obvious cues that humans used to express their emotions. It is a necessary aspect of social communication between humans in their daily lives. However, humans do hide their real emotions in certain circumstances. Therefore, facial micro-expression has been observed and analyzed to reveal the true human emotions. However, micro-expression is a complicated type of signal that manifests only briefly. Hence, machine learning techniques have been used to perform micro-expression recognition. This paper introduces a compact deep learning architecture to classify and recognize human emotions of three categories, which are positive, negative, and surprise. This study utilizes the deep learning approach so that optimal features of interest can be extracted even with a limited number of training samples. To further improve the recognition performance, a multi-scale module through the spatial pyramid pooling network is embedded into the compact network to capture facial expressions of various sizes. The base model is derived from the VGG-M model, which is then validated by using combined datasets of CASMEII, SMIC, and SAMM. Moreover, various configurations of the spatial pyramid pooling layer were analyzed to find out the most optimal network setting for the micro-expression recognition task. The experimental results show that the addition of a multiscale module has managed to increase the recognition performance. The best network configuration from the experiment is composed of five parallel network branches that are placed after the second layer of the base model with pooling kernel sizes of two, three, four, five, and six.

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“…CNN reduces the complexity of network models and the number of weights and has a unique advantage in image processing and speech recognition (8). CNN has four main operations (10), namely:…”

Section: B Convolutional Neural Networkmentioning

confidence: 99%

Robust License Plate Detection using Convolutional Neural Network

Astawa¹,

Caturbawa²,

Rudiastari³

et al. 2018

Proceedings of the International Conference on Science and Technology (ICST 2018)

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In general, the vehicle number plate detection system in an image should be able to overcome two problems, first how to determine the position of the vehicle number plate or which vehicle number plate is on an image and the second how big is the plate. A number of vehicle license plate detection methods have been proposed over the past two decades, and some have shown success in certain tasks. This study will detect the license plate number of vehicles using the CNN. The initial process is to create a training data license plate numbers using CNN processed on the server. Furthermore, the training data entered on the vehicle license plate detection applications. Vehicle license plate detection results will be displayed in percent accuracy. Based on the testing, the results obtained were very satisfactory with the accuracy of detection of vehicle license numbers as much as 98.19%, the remaining 1.81% of the plates were detected but not the image of the plate that was cropped. It shows that the method has been implemented on CNN that training data and license plate detection is very pretty accurate in detecting vehicle license plate. Implementation of CNN methods on android based mobile devices for data processing training and detection testing of vehicle license plate shows very satisfactory results.

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Automatic License Plate Recognition Technique using Convolutional Neural Network

Cited by 7 publications

References 6 publications

Automated License Plate Recognition for Resource-Constrained Environments

Automated License Plate Recognition for Resource-Constrained Environments

Micro Expression Recognition: Multi-scale Approach to Automatic Emotion Recognition by using Spatial Pyramid Pooling Module

Robust License Plate Detection using Convolutional Neural Network

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