  FPN (Feature Pyramid Network) has become a basic component of most SoTA one
stage object detectors. Many previous studies have repeatedly proved that FPN
can caputre better multi-scale feature maps to more precisely describe objects
if they are with different sizes. However, for most backbones such VGG, ResNet,
or DenseNet, the feature maps at each layer are downsized to their quarters due
to the pooling operation or convolutions with stride 2. The gap of
down-scaling-by-2 is large and makes its FPN not fuse the features smoothly.
This paper proposes a new SFPN (Synthetic Fusion Pyramid Network) arichtecture
which creates various synthetic layers between layers of the original FPN to
enhance the accuracy of light-weight CNN backones to extract objects' visual
features more accurately. Finally, experiments prove the SFPN architecture
outperforms either the large backbone VGG16, ResNet50 or light-weight backbones
such as MobilenetV2 based on AP score.
