Fused Faster RCNNs for Efficient Detection of the License Plates

Diyar Qader Sallem

Abstract


Automatic License Plate Detection and Recognition (ALPD-R) is an important application for traffic surveillance, traffic safety, security, services purposes and parking management. Generally, traditional image processing routines have been used in ALPD-R. In this paper, the Deep Learning (DL) is used to detect license plates in given images. More specifically, the Faster Regions with Convolutional Neutral Network (Faster- RCNN) architecture is used due to its various advantages. The proposed license plate detection method uses three Faster- RCNN modules where each faster RCNN module uses a pre-trained CNN models namely AlexNet, VGG16 and VGG19. Each Faster-RCNN module is trained independently and their results are fused in fusing layer. Fusing layer use average operator on the X and Y coordinates of the outputs of the Faster-RCNN modules and maximum operator is employed on the width and height outputs of the Faster-RCNN modules. A publicly available dataset is used in experiments. The accuracy is used as a performance indicator of the proposed method. For 100 testing images, the proposed method detects the exact location of license plates for 97 images. The accuracy of the proposed method is 97%.

Keywords


License plate detection Deep learning Faster- RCNN Vehicle images

References


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DOI: http://doi.org/10.11591/ijeecs.v19.i2.pp%25p
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