Feature Pyramid Network (FPN)
A Feature Pyramid Network (FPN) is a type of deep learning architecture designed to improve the performance of object detection tasks. It does this by creating a pyramid of feature maps at different scales, allowing the model to recognize objects of various sizes more effectively.
In traditional convolutional neural networks (CNNs), features are typically extracted at a single scale, which can lead to poor performance when detecting small or large objects. FPN addresses this limitation by introducing a top-down architecture with lateral connections that combine high-level semantic features with low-level features. This integration enhances the spatial resolution of feature maps, enabling the model to make more accurate predictions.
The architecture consists of two main components: a bottom-up pathway and a top-down pathway. The bottom-up pathway is responsible for feature extraction, where the network processes the input image through several layers to produce a hierarchy of features. The top-down pathway then progressively upsamples these features to create a feature pyramid, which combines information from different levels of the hierarchy.
This multi-scale approach allows FPN to effectively detect objects at various scales, making it particularly useful in applications like image segmentation and object detection. FPNs have been widely adopted in popular object detection frameworks such as Mask R-CNN and RetinaNet, contributing to significant improvements in accuracy and efficiency.