The development of scientific inquiry and research has yielded numerous benefits in the realm of intelligent traffic control systems, particularly in the realm of automatic license plate recognition for vehicles. The ...The development of scientific inquiry and research has yielded numerous benefits in the realm of intelligent traffic control systems, particularly in the realm of automatic license plate recognition for vehicles. The design of license plate recognition algorithms has undergone digitalization through the utilization of neural networks. In contemporary times, there is a growing demand for vehicle surveillance due to the need for efficient vehicle processing and traffic management. The design, development, and implementation of a license plate recognition system hold significant social, economic, and academic importance. The study aims to present contemporary methodologies and empirical findings pertaining to automated license plate recognition. The primary focus of the automatic license plate recognition algorithm was on image extraction, character segmentation, and recognition. The task of character segmentation has been identified as the most challenging function based on my observations. The license plate recognition project that we designed demonstrated the effectiveness of this method across various observed conditions. Particularly in low-light environments, such as during periods of limited illumination or inclement weather characterized by precipitation. The method has been subjected to testing using a sample size of fifty images, resulting in a 100% accuracy rate. The findings of this study demonstrate the project’s ability to effectively determine the optimal outcomes of simulations.展开更多
Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended objective.Precise controlling and management of traffic conditions,increased safety and surveillance,and enha...Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended objective.Precise controlling and management of traffic conditions,increased safety and surveillance,and enhanced incident avoidance and management should be top priorities in smart city management.At the same time,Vehicle License Plate Number Recognition(VLPNR)has become a hot research topic,owing to several real-time applications like automated toll fee processing,traffic law enforcement,private space access control,and road traffic surveillance.Automated VLPNR is a computer vision-based technique which is employed in the recognition of automobiles based on vehicle number plates.The current research paper presents an effective Deep Learning(DL)-based VLPNR called DLVLPNR model to identify and recognize the alphanumeric characters present in license plate.The proposed model involves two main stages namely,license plate detection and Tesseract-based character recognition.The detection of alphanumeric characters present in license plate takes place with the help of fast RCNN with Inception V2 model.Then,the characters in the detected number plate are extracted using Tesseract Optical Character Recognition(OCR)model.The performance of DL-VLPNR model was tested in this paper using two benchmark databases,and the experimental outcome established the superior performance of the model compared to other methods.展开更多
Recent technological advancements have been used to improve the quality of living in smart cities.At the same time,automated detection of vehicles can be utilized to reduce crime rate and improve public security.On th...Recent technological advancements have been used to improve the quality of living in smart cities.At the same time,automated detection of vehicles can be utilized to reduce crime rate and improve public security.On the other hand,the automatic identification of vehicle license plate(LP)character becomes an essential process to recognize vehicles in real time scenarios,which can be achieved by the exploitation of optimal deep learning(DL)approaches.In this article,a novel hybrid metaheuristic optimization based deep learning model for automated license plate character recognition(HMODL-ALPCR)technique has been presented for smart city environments.The major intention of the HMODL-ALPCR technique is to detect LPs and recognize the characters that exist in them.For effective LP detection process,mask regional convolutional neural network(Mask-RCNN)model is applied and the Inception with Residual Network(ResNet)-v2 as the baseline network.In addition,hybrid sunflower optimization with butterfly optimization algorithm(HSFO-BOA)is utilized for the hyperparameter tuning of the Inception-ResNetv2 model.Finally,Tesseract based character recognition model is applied to effectively recognize the characters present in the LPs.The experimental result analysis of the HMODL-ALPCR technique takes place against the benchmark dataset and the experimental outcomes pointed out the improved efficacy of the HMODL-ALPCR technique over the recent methods.展开更多
Traditional methods of license character extraction cannot meet the requirements of recognition accuracy and speed rendered by the video vehicular detection system. Therefore, a license plate localization method based...Traditional methods of license character extraction cannot meet the requirements of recognition accuracy and speed rendered by the video vehicular detection system. Therefore, a license plate localization method based on multi-scale edge detection and a character segmentation algorithm based on Markov random field model is presented. Results of experiments demonstrate that the method yields more accurate license character extraction in contrast to traditional localization method based on edge detection by difference operator and character segmentation based on threshold. The accuracy increases from 90% to 94% under preferable illumination, while under poor condition, it increases more than 5%. When the two improved algorithms are used, the accuracy and speed of automatic license recognition meet the system＇s requirement even under the noisy circumstance or uneven illumination.展开更多
The paper covers analysis and investigation of lighting automation system in low-traffic long-roads. The main objective is to provide optimal solution between expensive safe design that utilizes continuous street ligh...The paper covers analysis and investigation of lighting automation system in low-traffic long-roads. The main objective is to provide optimal solution between expensive safe design that utilizes continuous street lighting system at night for the entire road, or inexpensive design that sacrifices the safety, relying on using vehicles lighting, to eliminate the problem of high cost energy consumption during the night operation of the road. By taking into account both of these factors, smart lighting automation system is proposed using Pattern Recognition Technique applied on vehicle number-plates. In this proposal, the road is sectionalized into zones, and based on smart Pattern Recognition Technique, the control system of the road lighting illuminates only the zone that the vehicles pass through. Economic analysis is provided in this paper to support the value of using this design of lighting control system.展开更多
城市小汽车出行的时空特性是支撑城市交通规划设计与交通需求管理的重要基础。针对传统的以集计数据或抽样数据研究的局限性,本文基于车牌识别数据,全量感知车辆出行活动,分析城市中个体车辆的出行时空模式。首先,从数据中提取并分离车...城市小汽车出行的时空特性是支撑城市交通规划设计与交通需求管理的重要基础。针对传统的以集计数据或抽样数据研究的局限性,本文基于车牌识别数据,全量感知车辆出行活动,分析城市中个体车辆的出行时空模式。首先,从数据中提取并分离车辆出行链,获得小汽车出行的时间、空间、频率和拓扑特征,根据各时段停留点构造车辆出行活动序列。其次,融合兴趣点(Point of Interest, POI)数据识别出行起讫点关联的土地利用特性作为停留点特征,在出行活动序列上应用k-modes聚类算法挖掘出常规通勤模式、特殊通勤模式、短时活动模式和外来办事模式这4类30种小汽车出行模式。最后,对每一类模式的群体规模、特征和典型出行行为进行详细地分析讨论。结果表明,95%的车辆出行活动可以用不多于3条边组成的简单拓扑结构表示,其中,约30%的车辆可构造出行活动序列,并用k-modes聚类算法有效分离出各类机动车全天出行的时空模式。工作日车辆出行主要表现为常规通勤模式,休息日则以短时活动模式为主。通过对个体车辆的微观行为分析,结合出行拓扑结构和出行活动序列进行出行模式的挖掘,能够全面地反映城市机动车出行的实际情况,为精细化机动车出行行为分析与管控策略制定提供理论支撑。展开更多
This paper introduces the Security System based on loops and license plate recognition.With an in-depth discussion about the key technology in the vehicle detection and the video adjustment,the paper presents a method...This paper introduces the Security System based on loops and license plate recognition.With an in-depth discussion about the key technology in the vehicle detection and the video adjustment,the paper presents a methodof obtaining a target video of good quality in all weather.The practices show that the proposed system is an effective public security measure at the checkpost.展开更多
文摘The development of scientific inquiry and research has yielded numerous benefits in the realm of intelligent traffic control systems, particularly in the realm of automatic license plate recognition for vehicles. The design of license plate recognition algorithms has undergone digitalization through the utilization of neural networks. In contemporary times, there is a growing demand for vehicle surveillance due to the need for efficient vehicle processing and traffic management. The design, development, and implementation of a license plate recognition system hold significant social, economic, and academic importance. The study aims to present contemporary methodologies and empirical findings pertaining to automated license plate recognition. The primary focus of the automatic license plate recognition algorithm was on image extraction, character segmentation, and recognition. The task of character segmentation has been identified as the most challenging function based on my observations. The license plate recognition project that we designed demonstrated the effectiveness of this method across various observed conditions. Particularly in low-light environments, such as during periods of limited illumination or inclement weather characterized by precipitation. The method has been subjected to testing using a sample size of fifty images, resulting in a 100% accuracy rate. The findings of this study demonstrate the project’s ability to effectively determine the optimal outcomes of simulations.
基金This research was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-track Research Funding Program。
文摘Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended objective.Precise controlling and management of traffic conditions,increased safety and surveillance,and enhanced incident avoidance and management should be top priorities in smart city management.At the same time,Vehicle License Plate Number Recognition(VLPNR)has become a hot research topic,owing to several real-time applications like automated toll fee processing,traffic law enforcement,private space access control,and road traffic surveillance.Automated VLPNR is a computer vision-based technique which is employed in the recognition of automobiles based on vehicle number plates.The current research paper presents an effective Deep Learning(DL)-based VLPNR called DLVLPNR model to identify and recognize the alphanumeric characters present in license plate.The proposed model involves two main stages namely,license plate detection and Tesseract-based character recognition.The detection of alphanumeric characters present in license plate takes place with the help of fast RCNN with Inception V2 model.Then,the characters in the detected number plate are extracted using Tesseract Optical Character Recognition(OCR)model.The performance of DL-VLPNR model was tested in this paper using two benchmark databases,and the experimental outcome established the superior performance of the model compared to other methods.
文摘Recent technological advancements have been used to improve the quality of living in smart cities.At the same time,automated detection of vehicles can be utilized to reduce crime rate and improve public security.On the other hand,the automatic identification of vehicle license plate(LP)character becomes an essential process to recognize vehicles in real time scenarios,which can be achieved by the exploitation of optimal deep learning(DL)approaches.In this article,a novel hybrid metaheuristic optimization based deep learning model for automated license plate character recognition(HMODL-ALPCR)technique has been presented for smart city environments.The major intention of the HMODL-ALPCR technique is to detect LPs and recognize the characters that exist in them.For effective LP detection process,mask regional convolutional neural network(Mask-RCNN)model is applied and the Inception with Residual Network(ResNet)-v2 as the baseline network.In addition,hybrid sunflower optimization with butterfly optimization algorithm(HSFO-BOA)is utilized for the hyperparameter tuning of the Inception-ResNetv2 model.Finally,Tesseract based character recognition model is applied to effectively recognize the characters present in the LPs.The experimental result analysis of the HMODL-ALPCR technique takes place against the benchmark dataset and the experimental outcomes pointed out the improved efficacy of the HMODL-ALPCR technique over the recent methods.
基金Supported by Science Development Foundation of Tianjin (No. 033183311) .
文摘Traditional methods of license character extraction cannot meet the requirements of recognition accuracy and speed rendered by the video vehicular detection system. Therefore, a license plate localization method based on multi-scale edge detection and a character segmentation algorithm based on Markov random field model is presented. Results of experiments demonstrate that the method yields more accurate license character extraction in contrast to traditional localization method based on edge detection by difference operator and character segmentation based on threshold. The accuracy increases from 90% to 94% under preferable illumination, while under poor condition, it increases more than 5%. When the two improved algorithms are used, the accuracy and speed of automatic license recognition meet the system＇s requirement even under the noisy circumstance or uneven illumination.
文摘The paper covers analysis and investigation of lighting automation system in low-traffic long-roads. The main objective is to provide optimal solution between expensive safe design that utilizes continuous street lighting system at night for the entire road, or inexpensive design that sacrifices the safety, relying on using vehicles lighting, to eliminate the problem of high cost energy consumption during the night operation of the road. By taking into account both of these factors, smart lighting automation system is proposed using Pattern Recognition Technique applied on vehicle number-plates. In this proposal, the road is sectionalized into zones, and based on smart Pattern Recognition Technique, the control system of the road lighting illuminates only the zone that the vehicles pass through. Economic analysis is provided in this paper to support the value of using this design of lighting control system.
文摘城市小汽车出行的时空特性是支撑城市交通规划设计与交通需求管理的重要基础。针对传统的以集计数据或抽样数据研究的局限性,本文基于车牌识别数据,全量感知车辆出行活动,分析城市中个体车辆的出行时空模式。首先,从数据中提取并分离车辆出行链,获得小汽车出行的时间、空间、频率和拓扑特征,根据各时段停留点构造车辆出行活动序列。其次,融合兴趣点(Point of Interest, POI)数据识别出行起讫点关联的土地利用特性作为停留点特征,在出行活动序列上应用k-modes聚类算法挖掘出常规通勤模式、特殊通勤模式、短时活动模式和外来办事模式这4类30种小汽车出行模式。最后,对每一类模式的群体规模、特征和典型出行行为进行详细地分析讨论。结果表明,95%的车辆出行活动可以用不多于3条边组成的简单拓扑结构表示,其中,约30%的车辆可构造出行活动序列,并用k-modes聚类算法有效分离出各类机动车全天出行的时空模式。工作日车辆出行主要表现为常规通勤模式,休息日则以短时活动模式为主。通过对个体车辆的微观行为分析,结合出行拓扑结构和出行活动序列进行出行模式的挖掘,能够全面地反映城市机动车出行的实际情况,为精细化机动车出行行为分析与管控策略制定提供理论支撑。
文摘This paper introduces the Security System based on loops and license plate recognition.With an in-depth discussion about the key technology in the vehicle detection and the video adjustment,the paper presents a methodof obtaining a target video of good quality in all weather.The practices show that the proposed system is an effective public security measure at the checkpost.
