CNN‐RNN based method for license plate recognition

Shivakumara, Palaiahnakote; Tang, Dongqi; Asadzadehkaljahi, Maryam; Lü, Tong; Pal, Umapada; Anisi, Mohammad Hossein

doi:10.1049/trit.2018.1015

Cited by 76 publications

(53 citation statements)

References 17 publications

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“…Hybrid model is referred to as the combination of more than one type of deep learning models. Recent research has seen a number of such hybrid models applied in different application domains in smart cities, e.g., RNN + CNN for city transportation (travel time estimation) [44], healthcare (hand gesture recognition) [96] and public safety (license plate recognition) [97]; SAE + RNN for environment monitoring (PM2.5) [10]; and RNN + RBM for transportation (traffic congestion) [98], and etc.…”

Section: Hybrid Modelmentioning

confidence: 99%

A Survey on an Emerging Area: Deep Learning for Smart City Data

Chen

Wang

et al. 2019

IEEE Trans. Emerg. Top. Comput. Intell.

124

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Rapid urbanisation has brought about great challenges to our daily lives, such as traffic congestion, environmental pollution, energy consumption, public safety and so on. Research on smart cities aims to address these issues with various technologies developed for the Internet of Things. Very recently, the research focus has shifted towards processing of massive amount of data continuously generated within a city environment, e.g., physical and participatory sensing data on traffic flow, air quality, and healthcare. Techniques from computational intelligence have been applied to process and analyse such data, and to extract useful knowledge that helps citizens better understand their surroundings and informs city authorities to provide better and more efficient public services. Deep learning, as a relatively new paradigm in computational intelligence, has attracted substantial attention of the research community and demonstrated greater potential over traditional techniques. This paper provides a survey of the latest research on the convergence of deep learning and smart city from two perspectives: while the technique-oriented review pays attention to the popular and extended deep learning models, the application-oriented review emphasises the representative application domains in smart cities. Our study showed that there are still many challenges ahead for this emerging area owing to the complex nature of deep learning and wide coverage of smart city applications. We pointed out a number of future directions related to deep learning efficiency, emergent deep learning paradigms, knowledge fusion and privacy preservation, and hope these would move the relevant research one step further in creating truly distributed intelligence for smart cities.

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Section: Hybrid Modelmentioning

confidence: 99%

A Survey on an Emerging Area: Deep Learning for Smart City Data

Chen

Wang

et al. 2019

IEEE Trans. Emerg. Top. Comput. Intell.

124

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“…Wang et al [19] employed deep models to learn discriminative features for age estimation. Recently, CNNs have been used for license plate recognition in [20].…”

Section: Literature Reviewmentioning

confidence: 99%

The Impact of Asymmetric Left and Asymmetric Right Face Images on Accurate Age Estimation

Sajid

Ratyal

Ali

et al. 2019

Mathematical Problems in Engineering

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Aging affects left and right half face differently owing to numerous factors such as sleeping habits, exposure to sun light, and weaker face muscles of one side of face. In computer vision, age of a given face image is estimated using features that are correlated with age, such as moles, scars, and wrinkles. In this study we report the asymmetric aging of the left and right sides of face images and its impact on accurate age estimation. Left symmetric faces were perceived as younger while right symmetric faces were perceived as older when presented to the state-of-the-art age estimator. These findings show that facial aging is an asymmetric process which plays role in accurate facial age estimation. Experimental results on two large datasets verify the significance of using asymmetric right face image to estimate the age of a query face image more accurately compared to the corresponding original or left asymmetric face image.

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“…To capture the behavior of the gas desorption per unit mass in 30 min as a function of the particle size, temperature, gas pressure, coal sample moisture content, and coal sample forming pressure, an equation with a high degree of correlation needed to be fitted to the experimental data. Table 2: Factors and levels of orthogonal experiment L25 (5 6 ). Table 3: Orthogonal experimental design.…”

Section: Regression Analysismentioning

confidence: 99%

“…e gas desorption and diffusion process of coal is closely related to the prediction of coal and gas outburst and is mainly applied in the determination of the gas content in coal seams and gas desorption index of drill cuttings [3,4]. Regarding the mechanism of gas desorption and diffusion of coal, most scholars believe that the dynamic process of gas diffusion of coal at atmospheric pressure follows Fick's law of diffusion with the concentration gradient as the driving force, and the simplified model of homogeneous gas particle diffusion [5][6][7][8] is as follows:…”

Section: Introductionmentioning

confidence: 99%

Orthogonal Experimental Study on Multifactor Conditions for Gas Desorption in Coal

Wang

et al. 2019

Advances in Civil Engineering

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The isothermal adsorption experiment of coal is an important method to study the mechanism of coal gas desorption. The orthogonal experiment is used to compare the gas desorption mechanism of coal under multifactor conditions, such as the particle size, temperature, pressure, moisture content, and molding pressure. The sensitivity of five factors was used to conduct regression analysis. The sensitivity and influence degree of five factors on the coal gas desorption capacity were analyzed. The results showed the following: (1) the effect of the coal sample particle size, temperature, pressure, moisture content, and molding pressure of coal on the sensitivity of desorption capacity is shown as C (pressure) > B (temperature) > A (particle size) > D (moisture content) > E (molding pressure); (2) the regression analysis of various factors for gas adsorption indicates that the degree of correlation of the multivariate quadratic regression equation is higher compared to that of the multivariate one-time regression equation; and (3) the coal sample particle size, temperature, pressure, moisture content, and type of gas desorption can well represent the gas desorption capacity of a coal sample under various conditions. The smaller the particle size of the coal sample, the higher the ambient temperature, the higher the gas pressure, the lower the moisture content of the coal sample, and the greater the gas desorption per unit mass in 30 min after coal sample gas adsorption equilibrium. The orthogonal test results have important theoretical significance for guiding gas adsorption and desorption tests of coal. For coal and gas outburst prediction, the coal seam gas flow mechanism, coal gas content prediction, and calculation of the mining coal gas emission have important practical significance for gas explosion accident prevention.

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CNN‐RNN based method for license plate recognition

Cited by 76 publications

References 17 publications

A Survey on an Emerging Area: Deep Learning for Smart City Data

A Survey on an Emerging Area: Deep Learning for Smart City Data

The Impact of Asymmetric Left and Asymmetric Right Face Images on Accurate Age Estimation

Orthogonal Experimental Study on Multifactor Conditions for Gas Desorption in Coal

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