ANPR with YOLOV8

YOLO V8 is the latest model developed by the Ultralytics team. It’s a state-of-the-art YOLO model that transcends its predecessors in terms of both accuracy and efficiency. <img alt="" src="https://miro.medium.com/v2/resize:fit:630/1*6Sn93qyovio0qn3Y3q7Vgw.png" style="height:263px; width:700px" /> <a href="https://github.com/ultralytics/ultralytics" rel="noopener ugc nofollow" target="_blank">https://github.com/ultralytics/ultralytics</a> It’s easy to use and accessible from the command line or via the Python package. It offers out-of-the-box support for object detection, classification and segmentation tasks. It recently added native support for object tracking as well so we won’t have to deal with cloning repos of tracking algorithms. In this article, I will go through the steps of utilizing YOLOV8 to build an automatic number plate recognition(ANPR) tool. So let’s get started. <h1>Tracking vehicles:</h1> As we mentioned earlier, YOLOV8 has native tracking, so this step is pretty straightforward. First, install ultralytics package <pre> pip install ultralytics</pre> Then, we have to read the video frames with open cv and apply the model track method with the persist argument set to True to ensure the ids persist through the next frame. The model returns coordinates to draw a bounding box plus the id, label, and score <a href="https://pub.towardsai.net/anpr-with-yolov8-a4a5c5c4f1ea">Click Here</a>