Project 5 - Object detection I, introduction to YOLOv4
- James Canova
- Sep 8, 2021
- 1 min read
Project start date: 31 December 2021
Project finish date: 3 January 2022
Object Recognition: A general term to describe a collection of related computer vision tasks that involve identifying objects in digital photographs.
Image Classification: Predict the type or class of an object in an image.
Input: An image with a single object, such as a photograph.
Output: A class label (e.g. one or more integers that are mapped to class labels).
Object Localization: Locate the presence of objects in an image and indicate their location with a bounding box.
Input: An image with one or more objects, such as a photograph.
Output: One or more bounding boxes (e.g. defined by a point, width, and height).
Object Detection: Locate the presence of objects with a bounding box and types or classes of the located objects in an image.
Input: An image with one or more objects, such as a photograph.
Output: One or more bounding boxes (e.g. defined by a point, width, and height), and a class label for each bounding box.
YOLOv4 also displays the probability that the class label is correct. [added by blog author]
https://towardsdatascience.com/image-segmentation-part-1-9f3db1ac1c50
Object Segmentation: Image segmentation is a method in which a digital image is broken down into various subgroups calledImage segments which helps in reducing the complexity of the image to make further processing or analysis of the image simpler.
An example of image segmentation is shown in the image below. This won't be covered in this blog and is only mentioned for completeness.
Coco classes for which the neural network was trained:
If you have any problems or need clarification please contact me: jscanova@gmail.com
good job, lots of references.