Bounding boxes are a fundamental component of object detection, providing a rectangular region around objects of interest in an image. They are used to indicate the spatial location and size of an object, making it easier for the model to understand where the object is within the image. During training, bounding boxes, along with labels, serve as ground truth data, enabling the model to learn how to localize and classify objects. In practical applications, bounding boxes are used in tasks such as tracking objects in video feeds, autonomous vehicle navigation, and retail analytics.
What's the role of bounding boxes in object detection?
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