Vehicle recognition plays a vital role in intelligent transportation systems,law enforcement,access control,and security operations—domains that are becoming increasingly dynamic and complex.Despite advancements,most...Vehicle recognition plays a vital role in intelligent transportation systems,law enforcement,access control,and security operations—domains that are becoming increasingly dynamic and complex.Despite advancements,most existing solutions remain siloed,addressing individual tasks such as vehicle make and model recognition(VMMR),automatic number plate recognition(ANPR),and color classification separately.This fragmented approach limits real-world efficiency,leading to slower processing,reduced accuracy,and increased operational costs,particularly in traffic monitoring and surveillance scenarios.To address these limitations,we present a unified framework that consolidates all three recognition tasks into a single,lightweight system.The framework utilizes MobileNetV2 for efficient VMMR,YOLO(You Only Look Once)for accurate license plate detection,and histogram-based clustering in the HSV color space for precise color identification.Rather than optimizing each module in isolation,our approach emphasizes tight integration,enabling improved performance and reliability.The system also features adaptive image calibration and robust algorithmic enhancements to ensure consistent results under varying environmental conditions.Experimental evaluations demonstrate that the proposedmodel achieves a combined accuracy of 93.3%,outperforming traditional methods and offering practical scalability for deployment in real-world transportation infrastructures.展开更多
以信号周期为时间窗的路段行程时间估计对交通运行状况分析具有重要意义。通过匹配路段上下游交叉口的自动车牌识别(ANPR,Automatic Number Plate Recognition)数据可以得到车辆的路段行程时间,使用缺失数据集获得的周期车均行程时间难...以信号周期为时间窗的路段行程时间估计对交通运行状况分析具有重要意义。通过匹配路段上下游交叉口的自动车牌识别(ANPR,Automatic Number Plate Recognition)数据可以得到车辆的路段行程时间,使用缺失数据集获得的周期车均行程时间难以准确表征路段交通运行状况。因此本文提出一种基于周期的路段行程时间估计方法,该方法将匹配车辆的行程时间、到离上下游停止线的时刻、信号配时数据作为输入,建立基于最小二乘法的多段到达率行程时间模型,利用该模型对未匹配车辆行程时间进行估计。结果表明该方法能够较好地捕捉原数据特征,随着缺失车辆数的增多能够极大地减小周期车均行程时间误差,并且在79.99%的情况下有正收益,20.58%的情况下收益值大于10s。展开更多
Fuel station drive-offs,wherein the drivers simply drive off without paying,are a major issue in the UK(United Kingdom)due to rising fuel costs and financial hardships.The phenomenon has increased greatly over the las...Fuel station drive-offs,wherein the drivers simply drive off without paying,are a major issue in the UK(United Kingdom)due to rising fuel costs and financial hardships.The phenomenon has increased greatly over the last few years,with reports indicating a substantial increase in such events in the major cities.Traditional prevention measures such as Avutec and Driveoffalert rely primarily on expensive infrastructure and blacklisted databases.Such systems typically involve costly camera installation andmaintenance and are consequently out of the budget of small fuel stations.These conventional approaches also fall short regarding real-time recognition,particularly regarding first-time impostors using fictitious plates,which represent an increasingly significant proportion of such forgery.This research presents an AI(Artificial Intelligence)-driven detection system using the MOT(Ministry of Transport)History API(Application Programming Interface)to scan in real-time at gas stations to recognize and prevent such fraud.The system integrates various state-of-the-art technologies to offer a foolproof system.Using the latestYOLO(YouOnly Look Once)model to recognize number plates and EasyOCR(Optical Character Recognition)to recognize characters,the system correctly reads license plates in various environmental conditions like lighting,viewpoint,and weather conditions.This approach minimizes the utilization of expensive camera systems and employs cheaper ANPR(AutomaticNumber Plate Recognition)gear,availing existing installed surveillance cameras on filling stations.The system operates with a basic web-based application to notify operators of stolen vehicles in real-time,enabling them to react immediately.Real-world testing achieves 84%success with CCTV(Closed-Circuit Television)images,depicting its real-world applicability.The results indicate that the AI-driven solution offers a monumental leap compared to current practices,giving fuel stations a cost-effective and efficient means of reducing financial loss from drive-off incidents.展开更多
基金supported in part by Multimedia University Research Fellow under Grant MMUI/250008in part by Telekom Research and Development Sdn Bhd under Grant RDTC/241149.
文摘Vehicle recognition plays a vital role in intelligent transportation systems,law enforcement,access control,and security operations—domains that are becoming increasingly dynamic and complex.Despite advancements,most existing solutions remain siloed,addressing individual tasks such as vehicle make and model recognition(VMMR),automatic number plate recognition(ANPR),and color classification separately.This fragmented approach limits real-world efficiency,leading to slower processing,reduced accuracy,and increased operational costs,particularly in traffic monitoring and surveillance scenarios.To address these limitations,we present a unified framework that consolidates all three recognition tasks into a single,lightweight system.The framework utilizes MobileNetV2 for efficient VMMR,YOLO(You Only Look Once)for accurate license plate detection,and histogram-based clustering in the HSV color space for precise color identification.Rather than optimizing each module in isolation,our approach emphasizes tight integration,enabling improved performance and reliability.The system also features adaptive image calibration and robust algorithmic enhancements to ensure consistent results under varying environmental conditions.Experimental evaluations demonstrate that the proposedmodel achieves a combined accuracy of 93.3%,outperforming traditional methods and offering practical scalability for deployment in real-world transportation infrastructures.
文摘以信号周期为时间窗的路段行程时间估计对交通运行状况分析具有重要意义。通过匹配路段上下游交叉口的自动车牌识别(ANPR,Automatic Number Plate Recognition)数据可以得到车辆的路段行程时间,使用缺失数据集获得的周期车均行程时间难以准确表征路段交通运行状况。因此本文提出一种基于周期的路段行程时间估计方法,该方法将匹配车辆的行程时间、到离上下游停止线的时刻、信号配时数据作为输入,建立基于最小二乘法的多段到达率行程时间模型,利用该模型对未匹配车辆行程时间进行估计。结果表明该方法能够较好地捕捉原数据特征,随着缺失车辆数的增多能够极大地减小周期车均行程时间误差,并且在79.99%的情况下有正收益,20.58%的情况下收益值大于10s。
文摘Fuel station drive-offs,wherein the drivers simply drive off without paying,are a major issue in the UK(United Kingdom)due to rising fuel costs and financial hardships.The phenomenon has increased greatly over the last few years,with reports indicating a substantial increase in such events in the major cities.Traditional prevention measures such as Avutec and Driveoffalert rely primarily on expensive infrastructure and blacklisted databases.Such systems typically involve costly camera installation andmaintenance and are consequently out of the budget of small fuel stations.These conventional approaches also fall short regarding real-time recognition,particularly regarding first-time impostors using fictitious plates,which represent an increasingly significant proportion of such forgery.This research presents an AI(Artificial Intelligence)-driven detection system using the MOT(Ministry of Transport)History API(Application Programming Interface)to scan in real-time at gas stations to recognize and prevent such fraud.The system integrates various state-of-the-art technologies to offer a foolproof system.Using the latestYOLO(YouOnly Look Once)model to recognize number plates and EasyOCR(Optical Character Recognition)to recognize characters,the system correctly reads license plates in various environmental conditions like lighting,viewpoint,and weather conditions.This approach minimizes the utilization of expensive camera systems and employs cheaper ANPR(AutomaticNumber Plate Recognition)gear,availing existing installed surveillance cameras on filling stations.The system operates with a basic web-based application to notify operators of stolen vehicles in real-time,enabling them to react immediately.Real-world testing achieves 84%success with CCTV(Closed-Circuit Television)images,depicting its real-world applicability.The results indicate that the AI-driven solution offers a monumental leap compared to current practices,giving fuel stations a cost-effective and efficient means of reducing financial loss from drive-off incidents.
