An caja de anclaje is a concept utilizado en visión por computadora, particularly in detección de objetos tasks. It refers to a set of predefined bounding boxes that are generated over an image to help the model identify objects within that image. These boxes are often generated based on the aspect ratios and sizes of objects that the model is expected to detect.
En detección de objetos algorithms, such as Faster R-CNN or YOLO (You Only Look Once), anchor boxes serve as reference points for the model. During the training process, the model learns to adjust these anchor boxes to better fit the actual objects present in the images. Each anchor box can be associated with a specific class label, allowing the model to predict not only the location of an object but also its category.
Las cajas de anclaje son cruciales para mejorar la accuracy of object detection. They allow the model to handle variations in object sizes and shapes effectively. By using multiple anchor boxes of different scales and aspect ratios, the model can better adapt to the diversity of objects it encounters in real-world scenarios.
In practice, when an image is processed, the model evaluates each anchor box to determine its overlap with the ground truth bounding boxes of objects. This evaluation is often done using metrics like Intersección sobre Unión (IoU), which measures the overlap between the predicted boxes and the actual locations of objects. Based on this evaluation, the model can refine its predictions and improve its overall performance.