Deep Learning Approach for Vehicle Number Plate Recognition System with Image Enhancement Technique
Keywords:
CNN, Alexnet, MobileNetV2, recognition, detection, convolutionalAbstract
The number of automobiles and trucks on the road is continually rising, particularly in direct connection to the emergence of the industrial revolution and the expansion of the economy. Because of the proliferation of motor vehicles, there is a greater potential for the violation of traffic laws, which in turn increases the risk of both unintended collisions and criminal activity on the road. In order to address these problems, a sophisticated traffic monitoring system is required. The intelligent technology has the ability to significantly contribute to traffic management via the recognition of licence plates. As part of this work, we use convolutional neural networks (CNNs) based Alexnet and MobileNetV2 framework, a subset of the deep learning technology known as convolutional neural networks, to build a system for the automatic identification and recognition of licence plates. We also propose to improve both the frameworks by applying image enhancement techniques. Both the detection and identification of licence plates are integral parts of this system. A digital camera is used to capture the image of the vehicle during the detection phase. The Alexnet and MobileNetV2 framework then isolates only the licence plate from the whole image. After the licence plate number region has been removed, the low-resolution image is converted into a high-quality one by a process called super resolution. The convolutional layer of a CNN is used in conjunction with a super resolution technique to restore the original image's pixel quality. To separate the characters of a licence plate number, we employ a bounding box method. Features are extracted and labelled using the CNN technique during the recognition stage.
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