Real-time identification of vehicle license plate has become more practical within the last decade in many applications such as; storage and retrieval of vehicular inflow records, automatic toll collection, parking fee payment, traffic monitoring, tracking of moving vehicles, recovery of stolen vehicles etc. A lot of researches have been carried out on vehicles license plates detection and localization which has led to the development of new techniques and modification of existing techniques. In this paper, we present an improved Faster Region-based Convolutional Neural Network (R-CNN) for localizing vehicle license plates. The Faster R-CNN utilized inceptionV2 architecture. The region of interest pooling of the existing Faster R-CNN was replaced by region of interest align which improved the creation quality of region proposals for license plates localization. The performance of the model was evaluated using Mean Average Precision (mAP) obtained from the precision-recall (PR) curves that were computed during model training and accuracy of 99% was achieved. The performance of the system was evaluated via real-time testing with 100 vehicles and localization accuracy of 99% was achieved. An effective camera to vehicle distance was also established via real time testing at different camera to vehicle distances.


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    Title :

    Real-Time Localization of Vehicle License Plate using Improved Faster Region-Based Convolutional Neural Network


    Contributors:


    Publication date :

    2021-04-25


    Size :

    2608992 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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