Learn what object detection is, how it differs from image classification, and what types of models can perform it.
Table of Contents
Question
Object detection is a form of machine learning-based computer vision in which a model is trained to recognize individual types of objects in an image, and to identify their location in the image. True or False?
A. True
B. False
Answer
A. True
Explanation
Object detection is a form of machine learning-based computer vision in which a model is trained to recognize individual types of objects in an image, and to identify their location in the image.
The correct answer is A. True.
Object detection is a form of machine learning-based computer vision in which a model is trained to recognize individual types of objects in an image, and to identify their location in the image. This is different from image classification, which only assigns a single label to the whole image, without specifying the location or the number of objects. Object detection can be used for various applications, such as face detection, vehicle detection, pedestrian detection, etc.
To perform object detection, a model needs to learn two things: what are the possible classes of objects, and how to draw bounding boxes around them. A bounding box is a rectangular region that encloses an object in an image. The model outputs the coordinates of the bounding box, as well as a confidence score that indicates how likely the object belongs to a certain class. For example, a model may detect a dog in an image and output a bounding box with coordinates (x1, y1, x2, y2), where (x1, y1) is the top-left corner and (x2, y2) is the bottom-right corner of the box, and a confidence score of 0.95 that indicates the probability of the object being a dog.
There are different types of models that can perform object detection, such as region-based convolutional neural networks (R-CNN), single shot multibox detector (SSD), and you only look once (YOLO). These models use different techniques to extract features from the image, propose regions of interest, and classify and localize the objects. For more information, you can refer to this [article] that compares different object detection models.
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