YoloV8 (You Only Look Once Version 8) is an object detection algorithm that uses convolutional neural networks (CNN) to find objects in images, videos, webcams, and live stream videos.


There are several steps to the process:


Yolov8 Setup: You can set up Yolov8 in your environment so that it can do different things with your input, such as Image, Video, CCTV, and Webcam.
Note: The environment can be your own computer or a server, for example (You can run Yolov8 on Ubuntu, Yolov8 on CentOS, Linux, Unix, etc.).


The next step is to put in an image or video that you want to be looked at for different things.


Preprocessing: The input image is resized and normalised to a fixed size so that processing is always the same, but this step is not required. YoloV8 uses a deep convolutional neural network to pull out different things from an image or video. The network has been taught by looking at a large set of labelled images. During the training process for Yolov8, the network learns to find different types of objects in images.


Prediction: After extracting image features, the YoloV8 algorithm predicts bounding boxes around the detected objects. The Yolov8 model predicts the coordinates of the bounding box relative to the image size and the confidence score of the detection in a particular class of Object. The confidence score shows how likely it is that the particular object is in the image.


Non-maximum suppression: YoloV8 applies non-maximum suppression to remove duplicate detection in images and videos. We do this by comparing the confidence scores of bounding boxes that overlap and keeping the one with the highest score.


Output: The final output of the YoloV8 algorithm is a list of bounding boxes around the detected objects in the image, along with their corresponding class labels and confidence scores. This information can be used to draw boxes around the things in the image or do other things based on what was found.

In conclusion, YoloV8 is an efficient and effective object detection algorithm that can find multiple objects in images, videos, and real-time streams. Its high accuracy and speed make it useful for many things, like self-driving cars, surveillance systems, and robots.