Object detection is a fundamental task in computer vision, and deep learning has revolutionized this field. This page provides an overview of deep learning techniques used for object detection, with a focus on the English language content.

Key Techniques

  • Convolutional Neural Networks (CNNs): CNNs are the backbone of most object detection models. They are designed to automatically and adaptively learn spatial hierarchies of features from input images.

  • Region-based Methods: These methods divide the image into regions and classify each region as containing an object or not. Examples include R-CNN, Fast R-CNN, and Faster R-CNN.

  • Single Shot Detectors (SSDs): SSDs aim to detect objects in a single forward pass of the network, making them faster than region-based methods.

  • You Only Look Once (YOLO): YOLO is a popular SSD that has achieved state-of-the-art performance on various benchmarks.

Resources

For further reading, you can explore the following resources:

Images

Here are some images related to object detection:

Object Detection
CNN Structure
YOLO Detection