Real-Time Object Detection with YOLOv8 and OpenCV

YOLO (You Only Look Once) is one of the most popular object detection approaches. In this article, we will use YOLOv8 with OpenCV to perform real-time object detection through a webcam. Prerequisites Make sure you have: Python 3.8+ The ultralytics library for YOLOv8 The opencv-python library for webcam access Install the dependencies with pip: pip install ultralytics opencv-python Example Code The following Python example performs object detection from a webcam stream:

Updated 02 Sep 2025

YOLO (You Only Look Once) is one of the most popular object detection approaches. In this article, we will use YOLOv8 with OpenCV to perform real-time object detection through a webcam.

Prerequisites

Make sure you have:

Python 3.8+
The ultralytics library for YOLOv8
The opencv-python library for webcam access

Install the dependencies with pip:

pip install ultralytics opencv-python

Example Code

The following Python example performs object detection from a webcam stream:

from ultralytics import YOLO
import cv2

# Load the lightweight and fast YOLOv8 Nano model
model = YOLO("yolov8n.pt")

# Open the webcam (0 = default camera)
cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    if not ret:
        break

    # Run detection
    results = model(frame)

    for result in results:
        # Read bounding boxes, class IDs, and confidence scores
        boxes = result.boxes
        class_ids = boxes.cls
        confidences = boxes.conf

        # Visualize the result
        annotated_frame = result.plot()
        cv2.imshow("YOLOv8 Inference", annotated_frame)

    # Press 'q' to exit
    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

cap.release()
cv2.destroyAllWindows()

How It Works

Load the model YOLO("yolov8n.pt") loads the YOLOv8 Nano model, which is a lightweight option suitable for real-time experiments.
Open the webcam cv2.VideoCapture(0) accesses the default camera. If your system has multiple cameras, replace 0 with the appropriate camera index.
Run detection results = model(frame) performs inference on each captured frame and returns detection results that include bounding boxes, confidence scores, and class IDs.
Display the result result.plot() creates an annotated frame, which is displayed with cv2.imshow.

Conclusion

With a small amount of code, you can build a real-time object detection pipeline using YOLOv8 and OpenCV. The same approach can be adapted to live cameras, recorded video, or collections of images.

Possible next steps include:

Saving detection results to files
Running detection on recorded video
Building automated monitoring or analysis systems

Object Detection with YOLOv8: Using a Pre-Trained Model on Images

YOLOv8 can run object detection on images with a pre-trained model. YOLO (You Only Look Once) is a well-known object-detection algorithm, and YOLOv8 is designed for fast inference and accurate results. The ultralytics library provides the Python interface used below. Requirements Before you begin, make sure you have: Python The ultralytics library If it is not installed yet, install it with pip: pip install ultralytics Run YOLOv8 on an image This example loads a pre-trained YOLOv8 model, runs it on one image, and exposes the detection results:

SMS Spam Detection with BERT and PyTorch

BERT can classify SMS messages as spam or legitimate after a sequence-classification model has been fine-tuned on labeled examples. BERT (Bidirectional Encoder Representations from Transformers) is a widely used NLP model architecture that can be adapted to many text tasks, including classification. PyTorch and Hugging Face Transformers provide a convenient way to work with BERT models. Requirements Before you begin, make sure you have: Python PyTorch Hugging Face Transformers An SMS dataset, such as the SMS Spam Collection, if you plan to fine-tune a classifier Install the required libraries with:

Using YOLOv8 for Object Detection with Labels and Confidence Scores

In this article, we will use YOLOv8 to detect objects in an image and print each object’s label and confidence score. YOLO (You Only Look Once) has long been a popular choice for object detection, and YOLOv8 provides a convenient Python API through the ultralytics package. Here is how to use it. Prerequisites Make sure Python and the ultralytics library are installed on your system. If needed, install the package with pip: