Object Detection

[anhtuduong]

1. Image Preprocessing:

This is the initial step in object detection, where the input image is preprocessed to reduce noise and enhance important features. Common preprocessing techniques include resizing, normalization, and color space conversion.

2. Object Localization 2D:

This task involves identifying the location of objects in the image. It typically involves using techniques like bounding boxes, masks, or keypoints to define the location of the object.

3. Object Classification:

This task involves assigning a label or class to the detected object. This can be a binary classification (e.g., object vs. non-object) or a multi-class classification (e.g., car, person, tree, etc.).

4. Object Tracking:

This task involves tracking the movement of objects over time. It is often used in video object detection applications.

5. Object Segmentation:

This task involves identifying the boundaries of objects within the image. It can be used for more precise object detection and is often used in medical imaging applications.

6. Instance Segmentation:

This task involves identifying the boundaries of individual objects within the image, even when they are overlapping. It is a more challenging task than object segmentation but can provide more detailed information about the objects within the image.

7. Object Recognition:

This task involves identifying specific objects within an image, such as recognizing a particular brand of car or a specific person's face. It requires a more advanced level of object detection and classification.