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Smart city article details
| Title | Vehicle Registration Plate Recognition Using Yolov8 And Iot |
|---|---|
| ID_Doc | 60944 |
| Authors | Nadaf J.; Kadam A.; Jadhav P.; Kadam P.; Patil V.; Gaikwad M. |
| Year | 2025 |
| Published | 2025 1st International Conference on AIML-Applications for Engineering and Technology, ICAET 2025 |
| DOI | http://dx.doi.org/10.1109/ICAET63349.2025.10932268 |
| Abstract | Number Plate recognition or the vehicle License Plate recognition is an interesting topic of research in smart cities which utilizes Image Processing techniques. Since the number of cars is rising at an exponential rate, automated systems are supposed to store vehicle data for a variety of applications. In this intended algorithm, an efficient technique has been identified for recognition of different vehicle number registration plates. Through this technique, we can easily deal with problems like low light, irregular font number plates, wrong angles. We use the method of Optical Character Recognition to identify characters that are extracted from the registration plate. Even though this project works efficiently for common use cases, it has its own drawbacks and limitations even though we are not using deep learning or any other machine learning algorithms. One of the important steps in vehicle plate recognition is the character recognition which is a technique to obtain individual text characters. YOLOv8 (You Only Look Once), and Darknet- 53 are the methods that is used to extract the features of the data. In this model, the input data is the image of the vehicle that is captured from the camera sensors or cell phones and later cropped to display only the registration plate. We undergo data-pre-processing to improve the quality or the pixels of image on the number plate. Testing is done using two different models one is the model which is obtained with more pre-processing data and the other obtained without any pre-processing data. We evaluate two datasets of number plate images: one with no interference and one where the colour intensity of the image has been decreased to test, the accuracy of the model © 2025 IEEE. |
| Author Keywords | Darknet-53; Image processing; Object Detection; Optical Character Recognition (OCR); YOLOv8 |
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