  Feature pyramid network (FPN) has been an effective framework to extract
multi-scale features in object detection. However, current FPN-based methods
mostly suffer from the intrinsic flaw of channel reduction, which brings about
the loss of semantical information. And the miscellaneous fused feature maps
may cause serious aliasing effects. In this paper, we present a novel channel
enhancement feature pyramid network (CE-FPN) with three simple yet effective
modules to alleviate these problems. Specifically, inspired by sub-pixel
convolution, we propose a sub-pixel skip fusion method to perform both channel
enhancement and upsampling. Instead of the original 1x1 convolution and linear
upsampling, it mitigates the information loss due to channel reduction. Then we
propose a sub-pixel context enhancement module for extracting more feature
representations, which is superior to other context methods due to the
utilization of rich channel information by sub-pixel convolution. Furthermore,
a channel attention guided module is introduced to optimize the final
integrated features on each level, which alleviates the aliasing effect only
with a few computational burdens. Our experiments show that CE-FPN achieves
competitive performance compared to state-of-the-art FPN-based detectors on MS
COCO benchmark.
