Incense in China-Cultural Convergence and Ethnic Integration in the Perspective of Incense.Incense has witnessed the thousands of years of civilization of the Chinese nation,accompanied by the fostering of the Chinese...Incense in China-Cultural Convergence and Ethnic Integration in the Perspective of Incense.Incense has witnessed the thousands of years of civilization of the Chinese nation,accompanied by the fostering of the Chinese cultural spirit and the formation of philosophical thoughts.The cover story is written by Qin Yanchun,a researcher from the Institute of Chinese Culture of the Chinese National Academy of Arts,who tells us the story of various spices such as Liquidambar orientalis from the"Western Regions",and the cultural convergence and ethnic integration of people of different times in the perspective of spices,incense set,incense products and other license materials.展开更多
Every developing country relies on transportation,and there has been an exponential expansion in the development of various sorts of vehicles with various configurations,which is a major component strengthening the au...Every developing country relies on transportation,and there has been an exponential expansion in the development of various sorts of vehicles with various configurations,which is a major component strengthening the automobile sector.India is a developing country with increasing road traffic,which has resulted in challenges such as increased road accidents and traffic oversight issues.In the lack of a parametric technique for accurate vehicle recognition,which is a major worry in terms of reliability,high traffic density also leads to mayhem at checkpoints and toll plazas.A system that combines an intelligent domain approach with more sustainability indices is a better way to handle traffic density and transparency issues.The Automatic Licence Plate Recognition(ALPR)system is one of the components of the intelligent transportation system for traffic monitoring.This study is based on a comprehensive and detailed literature evaluation in the field of ALPR.The major goal of this study is to create an automatic pattern recognition system with various combinations and higher accuracy in order to increase the reliability and accuracy of identifying digits and alphabets on a car plate.The research is founded on the idea that image processing opens up a diverse environment with allied fields when employing distinct soft techniques for recognition.The properties of characters are employed to recognise the Indian licence plate in this study.For licence plate recognition,more than 200 images were analysed with various parameters and soft computing techniques were applied.In comparison to neural networks,a hybrid technique using a Convolution Neural Network(CNN)and a Support Vector Machine(SVM)classifier has a 98.45%efficiency.展开更多
License plate recognition technology use widely in intelligent trafficmanagement and control. Researchers have been committed to improving thespeed and accuracy of license plate recognition for nearly 30 years. This p...License plate recognition technology use widely in intelligent trafficmanagement and control. Researchers have been committed to improving thespeed and accuracy of license plate recognition for nearly 30 years. This paperis the first to propose combining the attention mechanism with YOLO-v5and LPRnet to construct a new license plate recognition model (LPR-CBAMNet).Through the attention mechanism CBAM(Convolutional Block AttentionModule), the importance of different feature channels in license platerecognition can be re-calibrated to obtain proper attention to features. Forceinformation to achieve the purpose of improving recognition speed andaccuracy. Experimental results show that the model construction methodis superior in speed and accuracy to traditional license plate recognitionalgorithms. The accuracy of the recognition model of the CBAM model isincreased by two percentage points to 97.2%, and the size of the constructedmodel is only 1.8 M, which can meet the requirements of real-time executionof embedded low-power devices. The codes for training and evaluating LPRCBAM-Net are available under the open-source MIT License at: https://github.com/To2rk/LPR-CBAM-Net.展开更多
Privacy and trust are significant issues in intelligent transportation systems(ITS).Data security is critical in ITS systems since sensitive user data is communicated to another user over the internet through wireless...Privacy and trust are significant issues in intelligent transportation systems(ITS).Data security is critical in ITS systems since sensitive user data is communicated to another user over the internet through wireless devices and routes such as radio channels,optical fiber,and blockchain technology.The Internet of Things(IoT)is a network of connected,interconnected gadgets.Privacy issues occasionally arise due to the amount of data generated.However,they have been primarily addressed by blockchain and smart contract technology.While there are still security issues with smart contracts,primarily due to the complexity of writing the code,there are still many challenges to consider when designing blockchain designs for the IoT environment.This study uses traditional blockchain technology with the“You Only Look Once”(YOLO)object detection method to accurately locate and identify license plates.While YOLO and blockchain technologies used for intelligent vehicle license plate recognition are promising,they have received limited research attention.Real-time object identification and recognition would be possible by combining a cutting-edge object detection technique with a regional convolutional neural network(RCNN)built with the tensor flow core open source libraries.This method works reasonably well for identifying any license plate.The Automatic License Plate Recognition(ALPR)approach delivered outstanding results in various datasets.First,with a recognition rate of 96.2%,our system(UFPR-ALPR)surpassed the previously used technology,consisting of 4500 frames and around 150 films.Second,a deep learning algorithm was trained to recognize images of license plate numbers using the UFPR-ALPR dataset.Third,the license plate’s characters were complicated for standard methods to identify because of the shifting lighting correctly.The proposed model,however,produced beneficial outcomes.展开更多
This paper introduces the third enhanced version of a genetic algorithm-based technique to allow fast and accurate detection of vehicle plate numbers(VPLN)in challenging image datasets.Since binarization of the input ...This paper introduces the third enhanced version of a genetic algorithm-based technique to allow fast and accurate detection of vehicle plate numbers(VPLN)in challenging image datasets.Since binarization of the input image is the most important and difficult step in the detection of VPLN,a hybrid technique is introduced that fuses the outputs of three fast techniques into a pool of connected components objects(CCO)and hence enriches the solution space with more solution candidates.Due to the combination of the outputs of the three binarization techniques,many CCOs are produced into the output pool from which one or more sequences are to be selected as candidate solutions.The pool is filtered and submitted to a new memetic algorithm to select the best fit sequence of CCOs based on an objective distance between the tested sequence and the defined geometrical relationship matrix that represents the layout of the VPLN symbols inside the concerned plate prototype.Using any of the previous versions will give moderate results but with very low speed.Hence,a new local search is added as a memetic operator to increase the fitness of the best chromosomes based on the linear arrangement of the license plate symbols.The memetic operator speeds up the convergence to the best solution and hence compensates for the overhead of the used hybrid binarization techniques and allows for real-time detection especially after using GPUs in implementing most of the used techniques.Also,a deep convolutional network is used to detect false positives to prevent fake detection of non-plate text or similar patterns.Various image samples with a wide range of scale,orientation,and illumination conditions have been experimented with to verify the effect of the new improvements.Encouraging results with 97.55%detection precision have been reported using the recent challenging public Chinese City Parking Dataset(CCPD)outperforming the author of the dataset by 3.05%and the state-of-the-art technique by 1.45%.展开更多
文摘Incense in China-Cultural Convergence and Ethnic Integration in the Perspective of Incense.Incense has witnessed the thousands of years of civilization of the Chinese nation,accompanied by the fostering of the Chinese cultural spirit and the formation of philosophical thoughts.The cover story is written by Qin Yanchun,a researcher from the Institute of Chinese Culture of the Chinese National Academy of Arts,who tells us the story of various spices such as Liquidambar orientalis from the"Western Regions",and the cultural convergence and ethnic integration of people of different times in the perspective of spices,incense set,incense products and other license materials.
基金supported by Researchers Supporting Program(TUMAProject-2021-14)AlMaarefa University,Riyadh,Saudi Arabia.Mohd Anul Haq would like to thank Deanship of Scientific Research at Majmaah University for supporting this work under Project No.R-2022-173.
文摘Every developing country relies on transportation,and there has been an exponential expansion in the development of various sorts of vehicles with various configurations,which is a major component strengthening the automobile sector.India is a developing country with increasing road traffic,which has resulted in challenges such as increased road accidents and traffic oversight issues.In the lack of a parametric technique for accurate vehicle recognition,which is a major worry in terms of reliability,high traffic density also leads to mayhem at checkpoints and toll plazas.A system that combines an intelligent domain approach with more sustainability indices is a better way to handle traffic density and transparency issues.The Automatic Licence Plate Recognition(ALPR)system is one of the components of the intelligent transportation system for traffic monitoring.This study is based on a comprehensive and detailed literature evaluation in the field of ALPR.The major goal of this study is to create an automatic pattern recognition system with various combinations and higher accuracy in order to increase the reliability and accuracy of identifying digits and alphabets on a car plate.The research is founded on the idea that image processing opens up a diverse environment with allied fields when employing distinct soft techniques for recognition.The properties of characters are employed to recognise the Indian licence plate in this study.For licence plate recognition,more than 200 images were analysed with various parameters and soft computing techniques were applied.In comparison to neural networks,a hybrid technique using a Convolution Neural Network(CNN)and a Support Vector Machine(SVM)classifier has a 98.45%efficiency.
基金supported in part by the Natural Science Foundation of Hainan Province under Grant 621MS017the National Natural Science Foundation of China under Grant U19B2044.
文摘License plate recognition technology use widely in intelligent trafficmanagement and control. Researchers have been committed to improving thespeed and accuracy of license plate recognition for nearly 30 years. This paperis the first to propose combining the attention mechanism with YOLO-v5and LPRnet to construct a new license plate recognition model (LPR-CBAMNet).Through the attention mechanism CBAM(Convolutional Block AttentionModule), the importance of different feature channels in license platerecognition can be re-calibrated to obtain proper attention to features. Forceinformation to achieve the purpose of improving recognition speed andaccuracy. Experimental results show that the model construction methodis superior in speed and accuracy to traditional license plate recognitionalgorithms. The accuracy of the recognition model of the CBAM model isincreased by two percentage points to 97.2%, and the size of the constructedmodel is only 1.8 M, which can meet the requirements of real-time executionof embedded low-power devices. The codes for training and evaluating LPRCBAM-Net are available under the open-source MIT License at: https://github.com/To2rk/LPR-CBAM-Net.
基金extend their appreciation to the deanship of scientific research at Shaqra University for funding this research work through the Project Number(SU-ANN-202248).
文摘Privacy and trust are significant issues in intelligent transportation systems(ITS).Data security is critical in ITS systems since sensitive user data is communicated to another user over the internet through wireless devices and routes such as radio channels,optical fiber,and blockchain technology.The Internet of Things(IoT)is a network of connected,interconnected gadgets.Privacy issues occasionally arise due to the amount of data generated.However,they have been primarily addressed by blockchain and smart contract technology.While there are still security issues with smart contracts,primarily due to the complexity of writing the code,there are still many challenges to consider when designing blockchain designs for the IoT environment.This study uses traditional blockchain technology with the“You Only Look Once”(YOLO)object detection method to accurately locate and identify license plates.While YOLO and blockchain technologies used for intelligent vehicle license plate recognition are promising,they have received limited research attention.Real-time object identification and recognition would be possible by combining a cutting-edge object detection technique with a regional convolutional neural network(RCNN)built with the tensor flow core open source libraries.This method works reasonably well for identifying any license plate.The Automatic License Plate Recognition(ALPR)approach delivered outstanding results in various datasets.First,with a recognition rate of 96.2%,our system(UFPR-ALPR)surpassed the previously used technology,consisting of 4500 frames and around 150 films.Second,a deep learning algorithm was trained to recognize images of license plate numbers using the UFPR-ALPR dataset.Third,the license plate’s characters were complicated for standard methods to identify because of the shifting lighting correctly.The proposed model,however,produced beneficial outcomes.
文摘This paper introduces the third enhanced version of a genetic algorithm-based technique to allow fast and accurate detection of vehicle plate numbers(VPLN)in challenging image datasets.Since binarization of the input image is the most important and difficult step in the detection of VPLN,a hybrid technique is introduced that fuses the outputs of three fast techniques into a pool of connected components objects(CCO)and hence enriches the solution space with more solution candidates.Due to the combination of the outputs of the three binarization techniques,many CCOs are produced into the output pool from which one or more sequences are to be selected as candidate solutions.The pool is filtered and submitted to a new memetic algorithm to select the best fit sequence of CCOs based on an objective distance between the tested sequence and the defined geometrical relationship matrix that represents the layout of the VPLN symbols inside the concerned plate prototype.Using any of the previous versions will give moderate results but with very low speed.Hence,a new local search is added as a memetic operator to increase the fitness of the best chromosomes based on the linear arrangement of the license plate symbols.The memetic operator speeds up the convergence to the best solution and hence compensates for the overhead of the used hybrid binarization techniques and allows for real-time detection especially after using GPUs in implementing most of the used techniques.Also,a deep convolutional network is used to detect false positives to prevent fake detection of non-plate text or similar patterns.Various image samples with a wide range of scale,orientation,and illumination conditions have been experimented with to verify the effect of the new improvements.Encouraging results with 97.55%detection precision have been reported using the recent challenging public Chinese City Parking Dataset(CCPD)outperforming the author of the dataset by 3.05%and the state-of-the-art technique by 1.45%.
