  The visual feature pyramid has proven its effectiveness and efficiency in
target detection tasks. Yet, current methodologies tend to overly emphasize
inter-layer feature interaction, neglecting the crucial aspect of intra-layer
feature adjustment. Experience underscores the significant advantages of
intra-layer feature interaction in enhancing target detection tasks. While some
approaches endeavor to learn condensed intra-layer feature representations
using attention mechanisms or visual transformers, they overlook the
incorporation of global information interaction. This oversight results in
increased false detections and missed targets.To address this critical issue,
this paper introduces the Global Feature Pyramid Network (GFPNet), an augmented
version of PAFPN that integrates global information for enhanced target
detection. Specifically, we leverage a lightweight MLP to capture global
feature information, utilize the VNC encoder to process these features, and
employ a parallel learnable mechanism to extract intra-layer features from the
input image. Building on this foundation, we retain the PAFPN method to
facilitate inter-layer feature interaction, extracting rich feature details
across various levels.Compared to conventional feature pyramids, GFPN not only
effectively focuses on inter-layer feature information but also captures global
feature details, fostering intra-layer feature interaction and generating a
more comprehensive and impactful feature representation. GFPN consistently
demonstrates performance improvements over object detection baselines.
