Feature Pyramid Network for object detection

Explained what is Feature Pyramid Network (FPN) and How to use it with Region Proposal Network.

Feature Pyramid Network
A Feature Pyramid Network, or FPN, is a feature extractor that takes a single-scale image of an arbitrary size as input, and outputs proportionally sized feature maps at multiple levels, in a fully convolutional fashion. This process is independent of the backbone convolutional architectures.

Feature Pyramid Networks (FPN) is used for multi-scale features
FPN is used to improve the quality of features. We do that by merging the high resolution features with the low resolution features.
The high-resolution maps have low-level features and The low-resolution maps have high-level features.

The features at early layers are of high resolution but as the network goes deeper, The last layers have more informational features but they are very low resolution features.
Some of the detectors that use FPNs are RetinaNet, PANet, NAS-FPN, etc.

You can find that small objects are detected at different scale and the larger ones at different scale. This is exactly what feature pyramid network aims for. This is a multi-scale network.

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