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- W4367017280 abstract "The combination of car model classification and accelerated advances in computer vision appears to have the potential to significantly revolutionize automated transportation systems. Pattern recognition and image processing-based car classification methods have been proposed in recent years to improve the performance of smart highway toll collection and monitoring of traffic systems. Unfortunately, all these algorithms are trained on a inadequate number of features derived from tiny datasets, which do not considered for real-time road traffic circumstances. To address these difficulties, proposed system presents a classification model for vehicle based on convolutional neural networks to enhance the resilience of car classifier model in real-time applications. To achieve real-time robustness in classification systems, this proposed system offers a Stanford car dataset of 16,185 images classified into 157 common vehicle classes. To begin, the car dataset was trained using Inception-v3, VGG16, and ResNet50 to evaluate their accuracy based on performance. As a result, a comparative study was carried out to assess the effectiveness of the proposed system." @default.
- W4367017280 created "2023-04-27" @default.
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- W4367017280 date "2022-01-01" @default.
- W4367017280 modified "2023-10-14" @default.
- W4367017280 title "Image processing and pattern recognition-based car classification for intelligent transportation system" @default.
- W4367017280 doi "https://doi.org/10.1049/icp.2023.0520" @default.
- W4367017280 hasPublicationYear "2022" @default.
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