Object detection is a crucial task in computer vision, enabling systems to identify and locate objects within an image or video. This page provides a collection of tutorials to help you get started with object detection.

Tutorials List

Here are some popular object detection tutorials:

  • TensorFlow Object Detection API: This tutorial walks you through setting up the TensorFlow Object Detection API and building a custom object detection model. Read more

  • YOLO (You Only Look Once): YOLO is a real-time object detection system. This tutorial will guide you through implementing YOLO in Python. Read more

  • SSD (Single Shot MultiBox Detector): SSD is an efficient object detection method. Learn how to use SSD with this tutorial. Read more

Useful Resources

  • OpenCV: OpenCV is a powerful computer vision library that includes object detection capabilities. Learn more

  • Pillow: Pillow is a Python Imaging Library that makes it easy to work with images. Read more

Example

Here's a simple example of how to use object detection with Python and OpenCV:

import cv2

# Load the pre-trained model
net = cv2.dnn.readNet('yolov3.weights', 'yolov3.cfg')

# Load the image
image = cv2.imread('image.jpg')

# Preprocess the image
blob = cv2.dnn.blobFromImage(image, 1/255, (416, 416), (0, 0, 0), True, crop=False)

# Perform object detection
net.setInput(blob)
outputs = net.forward(net.getUnconnectedOutLayersNames())

# Process the outputs
# ...

Object Detection Example

Conclusion

Object detection is a complex but rewarding field. By following these tutorials and resources, you can start building your own object detection models and applications.