Object detection is a fundamental task in computer vision, where the goal is to identify and locate objects within an image or a video. This technology has numerous applications, from autonomous vehicles to security systems.

Key Concepts

  • Bounding Boxes: These are rectangles drawn around the detected objects to indicate their location within the image.
  • Confidence Scores: These scores represent the probability that an object is correctly identified.
  • Non-Maximum Suppression (NMS): This technique is used to eliminate redundant detections and keep only the most confident ones.

Techniques

  • Traditional Methods: These include techniques like edge detection, segmentation, and template matching.
  • Deep Learning Approaches: Convolutional Neural Networks (CNNs) have become the standard for object detection, with architectures like Faster R-CNN, YOLO, and SSD being widely used.

Resources

For further reading on object detection, you might want to check out our Deep Learning Tutorial.

Example

Here's an example of a bounding box around a detected object in an image:

![Example Bounding Box](https://cloud-image.ullrai.com/q/object_detection_bounding_box/)

If you're interested in the latest advancements in object detection, we recommend exploring this research paper on EfficientDet.