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Title	Yolov5 For Automatic License Plate Recognition In Smart Cities
ID_Doc	62123
Authors	Raza A.; Badidi E.; Badidi B.; Al Zahmi S.
Year	2023
Published	Lecture Notes in Networks and Systems, 768 LNNS
DOI	http://dx.doi.org/10.1007/978-3-031-42317-8_8
Abstract	Recognition and extraction of license plate information from still images or videos are the basis of modern traffic and security systems. Automatic License Plate Recognition (ALPR) is transforming public safety and transportation in many ways. Such license plate recognition systems enable advanced solutions for toll roads, offer significant operational cost savings through automation, and even open up new market opportunities, such as license plate readers mounted on police vehicles. In this work, we implement license plate recognition based on convolutional neural networks to achieve high accuracy. You Only Look Once (YOLO) algorithm is employed for detection, while, an Optical Character Recognition (OCR) technique is used for text recognition. We use four variants of the YOLOv5 for license plate detection and the EasyOCR for license plate recognition. The results show that YOLOv5x (extra-large) achieves a Mean Average Precision (mAP) of nearly 82% in detecting vehicles and license plates in an image. Furthermore, the recognition of the letters on license plates is achieved with a confidence score of nearly 65%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Author Keywords	Convolutional neural networks; Deep learning; Object detection; Object recognition

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