GMDNet: Fusion Network Based on Global Multi-Scale Detailed Information

Single-stage object detection networks are widely applied in various scenarios due to their high precision and speed. These networks typically consist of three parts structurally: backbone, neck, and head. Among these, multi-scale feature fusion in the neck is a crucial step to enhance detection effectiveness in object detection. Multi-scale feature fusion typically involves combining features from different layers through addition or concatenation. However, such methods often focus solely on the shared functionality of neighboring scale features, neglecting detailed information from higher-level features, leading to significant information loss. To address these issues, this paper proposes a novel network called GMDNet, comprising two key modules: a Global Information Sharing Module (GISM) and a Detailed Information Extraction Module (DIEM). GISM addresses different scale features with high and low-level branches, aligning and merging information across layers using structural reparameterization and Transformer techniques to enhance global information flow among different hierarchical features. DIEM extracts detailed information using three-feature fusion mapping and further integrates multi-scale information with detailed features using channel and positional attention mechanisms, effectively reintegrating previously overlooked details. Experiments conducted on multiple public datasets demonstrate that the proposed algorithm outperforms state-of-the-art methods and achieves an ideal balance between speed and accuracy across all model scales.