scholarly journals Deep Learning Based Ethiopian Car’s License Plate Detection and Recognition

2020 ◽  
Vol 8 (6) ◽  
pp. 5730-5737
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Learning Model ◽  
Motor Vehicles ◽  
License Plate ◽  
License Plate Recognition ◽  
Computer Algorithms ◽  
Wide Range ◽  
Processing Techniques ◽  
Deep Learning Model

Digital Image Processing is application of computer algorithms to process, manipulate and interpret images. As a field it is playing an increasingly important role in many aspects of people’s daily life. Even though Image Processing has accomplished a great deal on its own, nowadays researches are being conducted in using it with Deep Learning (which is part of a broader family, Machine Learning) to achieve better performance in detecting and classifying objects in an image. Car’s License Plate Recognition is one of the hottest research topics in the domain of Image Processing (Computer Vision). It is having wide range of applications since license number is the primary and mandatory identifier of motor vehicles. When it comes to license plates in Ethiopia, they have unique features like Amharic characters, differing dimensions and plate formats. Although there is a research conducted on ELPR, it was attempted using the conventional image processing techniques but never with deep learning. In this proposed research an attempt is going to be made in tackling the problem of ELPR with deep learning and image processing. Tensorflow is going to be used in building the deep learning model and all the image processing is going to be done with OpenCV-Python. So, at the end of this research a deep learning model that recognizes Ethiopian license plates with better accuracy is going to be built.

Detection of solder paste defects with an optimization‐based deep learning model using image processing techniques

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ali Sezer ◽  
Aytaç Altan
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Learning Model ◽  
Production Costs ◽  
Solder Paste ◽  
Data Set ◽  
Content Type ◽  
Image Processing Techniques ◽  
Processing Techniques ◽  
Deep Learning Model

Purpose In the production processes of electronic devices, production activities are interrupted due to the problems caused by soldering defects during the assembly of surface-mounted elements on printed circuit boards (PCBs), and this leads to an increase in production costs. In solder paste applications, defects that may occur in electronic cards are usually noticed at the last stage of the production process. This situation reduces the efficiency of production and causes delays in the delivery schedule of critical systems. This study aims to overcome these problems, optimization based deep learning model has been proposed by using 2D signal processing methods. Design/methodology/approach An optimization-based deep learning model is proposed by using image-processing techniques to detect solder paste defects on PCBs with high performance at an early stage. Convolutional neural network, one of the deep learning methods, is trained using the data set obtained for this study, and pad regions on PCB are classified. Findings A total of six types of classes used in the study consist of uncorrectable soldering, missing soldering, excess soldering, short circuit, undefined object and correct soldering, which are frequently used in the literature. The validity of the model has been tested on the data set consisting of 648 test data. Originality/value The effect of image processing and optimization methods on model performance is examined. With the help of the proposed model, defective solder paste areas on PCBs are detected, and these regions are visualized by taking them into a frame.

scholarly journals Automatic Vehicle License Plate Recognition Using Optimal Deep Learning Model

2021 ◽  
Vol 67 (2) ◽  
pp. 1881-1897
Author(s):  
Thavavel Vaiyapuri ◽  
Sachi Nandan Mohanty ◽  
M. Sivaram ◽  
Irina V. Pustokhina ◽  
Denis A. Pustokhin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
License Plate ◽  
License Plate Recognition ◽  
Vehicle License Plate Recognition ◽  
Deep Learning Model

scholarly journals DiPLIP: Distributed Parallel Processing Platform for Stream Image Processing Based on Deep Learning Model Inference

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1664
Author(s):  
Yoon-Ki Kim ◽  
Yongsung Kim
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Parallel Processing ◽  
Real Time ◽  
Large Scale ◽  
Learning Model ◽  
Streaming Data ◽  
Model Inference ◽  
Processing Platform ◽  
Deep Learning Model

Recently, as the amount of real-time video streaming data has increased, distributed parallel processing systems have rapidly evolved to process large-scale data. In addition, with an increase in the scale of computing resources constituting the distributed parallel processing system, the orchestration of technology has become crucial for proper management of computing resources, in terms of allocating computing resources, setting up a programming environment, and deploying user applications. In this paper, we present a new distributed parallel processing platform for real-time large-scale image processing based on deep learning model inference, called DiPLIP. It provides a scheme for large-scale real-time image inference using buffer layer and a scalable parallel processing environment according to the size of the stream image. It allows users to easily process trained deep learning models for processing real-time images in a distributed parallel processing environment at high speeds, through the distribution of the virtual machine container.

scholarly journals Recognition of Vehicle License Plates Based on Image Processing

2021 ◽  
Vol 11 (14) ◽  
pp. 6292
Author(s):  
Tae-Gu Kim ◽  
Byoung-Ju Yun ◽  
Tae-Hun Kim ◽  
Jae-Young Lee ◽  
Kil-Houm Park ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Recognition Rate ◽  
Super Resolution ◽  
Learning Model ◽  
Distortion Correction ◽  
License Plate ◽  
Generative Adversarial Network ◽  
Perspective Distortion ◽  
Deep Learning Model

In this study, we have proposed an algorithm that solves the problems which occur during the recognition of a vehicle license plate through closed-circuit television (CCTV) by using a deep learning model trained with a general database. The deep learning model which is commonly used suffers with a disadvantage of low recognition rate in the tilted and low-resolution images, as it is trained with images acquired from the front of the license plate. Furthermore, the vehicle images acquired by using CCTV have issues such as limitation of resolution and perspective distortion. Such factors make it difficult to apply the commonly used deep learning model. To improve the recognition rate, an algorithm which is a combination of the super-resolution generative adversarial network (SRGAN) model, and the perspective distortion correction algorithm is proposed in this paper. The accuracy of the proposed algorithm was verified with a character recognition algorithm YOLO v2, and the recognition rate of the vehicle license plate image was improved 8.8% from the original images.

scholarly journals UbiComb: A Hybrid Deep Learning Model for Predicting Plant-Specific Protein Ubiquitylation Sites

Genes ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 717
Author(s):  
Arslan Siraj ◽  
Dae Yeong Lim ◽  
Hilal Tayara ◽  
Kil To Chong
Keyword(s):  
Mass Spectrometry ◽  
Deep Learning ◽  
Large Scale ◽  
Critical Role ◽  
Learning Model ◽  
Mass Spectrometry Analysis ◽  
Independent Dataset ◽  
Effective Manner ◽  
Wide Range ◽  
Deep Learning Model

Protein ubiquitylation is an essential post-translational modification process that performs a critical role in a wide range of biological functions, even a degenerative role in certain diseases, and is consequently used as a promising target for the treatment of various diseases. Owing to the significant role of protein ubiquitylation, these sites can be identified by enzymatic approaches, mass spectrometry analysis, and combinations of multidimensional liquid chromatography and tandem mass spectrometry. However, these large-scale experimental screening techniques are time consuming, expensive, and laborious. To overcome the drawbacks of experimental methods, machine learning and deep learning-based predictors were considered for prediction in a timely and cost-effective manner. In the literature, several computational predictors have been published across species; however, predictors are species-specific because of the unclear patterns in different species. In this study, we proposed a novel approach for predicting plant ubiquitylation sites using a hybrid deep learning model by utilizing convolutional neural network and long short-term memory. The proposed method uses the actual protein sequence and physicochemical properties as inputs to the model and provides more robust predictions. The proposed predictor achieved the best result with accuracy values of 80% and 81% and F-scores of 79% and 82% on the 10-fold cross-validation and an independent dataset, respectively. Moreover, we also compared the testing of the independent dataset with popular ubiquitylation predictors; the results demonstrate that our model significantly outperforms the other methods in prediction classification results.

INSPECTION OF DEFECTIVE PRODUCTS IN THE INDUSTRY WITH IMAGE PROCESSING BASED ON DEEP LEARNING METHODS

2021 ◽  
Vol 0 (15) ◽  
pp. 0-0
Author(s):  
Tuğba Özge ONUR
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Control Systems ◽  
Learning Model ◽  
Submersible Pump ◽  
Learning Methods ◽  
Pump Impeller ◽  
Maximum Accuracy ◽  
Testing Stage ◽  
Deep Learning Model

Aim: This paper studies to detect the undetectable defects in casting such as air holes, pinholes, burrs, tensile defects, mold material defects, metal casting defects, metallurgical defects, and etc. Through images via deep learning methods. Method: In the study, an automatic reading review for submersible pump impeller is proposed and a deep learning model is developed. The data set of the images include defective and smooth submersible pump impeller has been utilized to prove the performance of the designed network architecture. Results: According to the obtained results, maximum accuracy of 89% for the classifier has been achieved in the training stage and it reached a maximum accuracy of 93% in the testing stage. Conclusion: Along with the developing systems, quality control systems have been evolved through image processing. Thanks to the image processing-based control systems, the capacity and efficiency of the production facilities can be increased and perfect products can be delivered to the end-user by making precise measurements. In this study, since casting is an important and frequently used process in the industry, its defects are evaluated and a deep learning model to automate the review process and detect the defective products of submersible pump impellers is presented. The success rate of 89% obtained show that the defective product inspection in the industry can be performed over the images by using a convolutional neural network (CNN) architecture.

scholarly journals Underwater Topography Inversion in Liaodong Shoal Based on GRU Deep Learning Model

2020 ◽  
Vol 12 (24) ◽  
pp. 4068
Author(s):  
Zihao Leng ◽  
Jie Zhang ◽  
Yi Ma ◽  
Jingyu Zhang
Keyword(s):  
Deep Learning ◽  
Water Depth ◽  
Learning Model ◽  
Water Area ◽  
Accuracy Evaluation ◽  
Underwater Topography ◽  
Wide Range ◽  
Log Linear ◽  
Deep Learning Model ◽  
New Framework

The Liaodong Shoal in the east of the Bohai Sea has obvious water depth variation. The clear shallow water area and deep turbid area coexist, which is characterized by complex submarine topography. The traditional semi-theoretical and semi-empirical models are often difficult to provide optimal inversion results. In this paper, based on the traditional principle of water depth inversion in shallow areas, a new framework is proposed in combination with the deep turbid sea area. This new framework extends the application of traditional optical water depth inversion methods, can meet the needs of the depth inversion work in the composite sea environment. Moreover, the gate recurrent unit (GRU) deep-learning model is introduced to approximate the unified inversion model by numerical calculation. In this paper, based on the above-mentioned inversion framework, the water depth inversion work is processed by using the wide range images of GF-1 satellite, then the relevant analysis and accuracy evaluation are carried out. The results show that: (1) for the overall water depth inversion, the determination coefficient R2 is higher than 0.9 and the MRE is lower than 20% are obtained, and the evaluation index shows that the GRU model can better retrieve the underwater topography of this region. (2) Compared with the traditional log-linear model, Stumpf model, and multi-layer feedforward neural network, the GRU model was significantly improved in various evaluation indices. (3) The model has the best inversion performance in the 24–32 m-depth section, with a MRE of about 4% and a MAE of about 1.42 m, which is more suitable for the inversion work in the comparative section area. (4) The inversion diagram indicates that this model can well reflect the regional seabed characteristics of multiple radial sand ridges, and the overall inversion result is excellent and practical.

ENHANCING THE ACCURACY OF LICENSE PLATE RECOGNITION SYSTEMS WITH THE ANGLE RECOVERY METHOD

2013 ◽  
Vol 27 (08) ◽  
pp. 1350025
Author(s):  
WERNHUAR TARNG ◽  
CHIEN-LUNG LI
Keyword(s):  
Recognition Rate ◽  
Recognition System ◽  
Motor Vehicles ◽  
License Plate ◽  
License Plate Recognition ◽  
Plane Rotation ◽  
Recovery Method ◽  
Rotation Method ◽  
Wide Range ◽  
Recognition Systems

The motor vehicle is an important way of transportation for modern people, and its license plate is just like our identification cards which can be used for effective management of motor vehicles. Hence, the development of a recognition system for license plates can reduce the workload of managing motor vehicles. A license plate recognition system based on computer vision often causes recognition errors due to the plate's angle problem and thus needs to be assisted by manual recognition. In this study, a recovery method for license plate images with large angles is proposed based on perspective projection to increase the recognition accuracy. The experimental results show that applying the angle recovery method to a license plate recognition system can reduce its errors, especially for license plate images with large angles. For the case of small angles, a recognition rate of 89% can be achieved by the angle recovery method, slightly higher than that of the plane rotation method (86%). For the case of a wide range of different angles, the average recognition rate achieved by the angle recovery method is 87.5%, much higher than that of the plane rotation method (57.5%). Thus, the angle recovery method is effective for enhancing the accuracy of license plate recognition systems.

Detecting Product Adoption Intentions via Multiview Deep Learning

2021 ◽  
Author(s):  
Zhu Zhang ◽  
Xuan Wei ◽  
Xiaolong Zheng ◽  
Qiudan Li ◽  
Daniel Dajun Zeng
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Purchase Intention ◽  
Learning Model ◽  
Coarse Grained ◽  
Product Adoption ◽  
Specific Product ◽  
Wide Range ◽  
Deep Learning Model ◽  
Adoption Intention

Detecting product adoption intentions on social media could yield significant value in a wide range of applications, such as personalized recommendations and targeted marketing. In the literature, no study has explored the detection of product adoption intentions on social media, and only a few relevant studies have focused on purchase intention detection for products in one or several categories. Focusing on a product category rather than a specific product is too coarse-grained for precise advertising. Additionally, existing studies primarily focus on using one type of text representation in target social media posts, ignoring the major yet unexplored potential of fusing different text representations. In this paper, we first formulate the problem of product adoption intention mining and demonstrate the necessity of studying this problem and its practical value. To detect a product adoption intention for an individual product, we propose a novel and general multiview deep learning model that simultaneously taps into the capability of multiview learning in leveraging different representations and deep learning in learning latent data representations using a flexible nonlinear transformation. Specifically, the proposed model leverages three different text representations from a multiview perspective and takes advantage of local and long-term word relations by integrating convolutional neural network (CNN) and long short-term memory (LSTM) modules. Extensive experiments on three Twitter datasets demonstrate the effectiveness of the proposed multiview deep learning model compared with the existing benchmark methods. This study also significantly contributes research insights to the literature about intention mining and provides business value to relevant stakeholders such as product providers.

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