Pronunciation: [ˈankə bˈɒks] (IPA) 
The spelling of the word "anchor box" is quite straightforward. In IPA phonetic transcription, it is written as /ˈæŋkər bɒks/. The first syllable has the "æ" sound as in "cat," while the second syllable has the "ə" sound as in "roses." The "k" and "r" sounds blend together to form the "kr" sound. The "bo" in "box" has the "ɒ" sound as in "hot," and the "ks" at the end is pronounced as a sharp "ks" sound, as in "ticks."
Anchor box is a computational object detection technique used in computer vision and image processing tasks. Also known as a default box or prior box, an anchor box represents a fixed-size bounding box that acts as a template for object localization in an image. It is an essential component of the popular object detection algorithm called Faster R-CNN (Region-Convolutional Neural Network).
In the context of object detection, an anchor box serves as a reference point for detecting and localizing objects of various sizes and aspect ratios within an image. It provides a set of predefined boxes that are overlaid onto the input image at different spatial positions. These anchor boxes span the entire image and are generated at multiple scales and aspect ratios to capture objects of different shapes and sizes.
During the training process, the anchor boxes are matched with ground truth bounding boxes to identify regions of interest (RoIs) based on their overlap. This allows the algorithm to learn and predict the presence, class, and precise location of objects in an image. By adjusting the anchor box parameters such as scale and aspect ratio, the algorithm can adapt to different object sizes and shapes.
The use of anchor boxes helps improve both detection accuracy and efficiency in object detection tasks. They provide a versatile framework for handling objects of varying scales and aspect ratios, making it possible to detect and localize objects accurately and efficiently in complex scenes.
