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
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.
