MGDFIS: Multi-scale Global-detail Feature Integration Strategy for Small Object Detection
Journal:
arXiv
Published Date:
Jun 15, 2025
Abstract
Small object detection in UAV imagery is crucial for applications such as
search-and-rescue, traffic monitoring, and environmental surveillance, but it
is hampered by tiny object size, low signal-to-noise ratios, and limited
feature extraction. Existing multi-scale fusion methods help, but add
computational burden and blur fine details, making small object detection in
cluttered scenes difficult. To overcome these challenges, we propose the
Multi-scale Global-detail Feature Integration Strategy (MGDFIS), a unified
fusion framework that tightly couples global context with local detail to boost
detection performance while maintaining efficiency. MGDFIS comprises three
synergistic modules: the FusionLock-TSS Attention Module, which marries
token-statistics self-attention with DynamicTanh normalization to highlight
spectral and spatial cues at minimal cost; the Global-detail Integration
Module, which fuses multi-scale context via directional convolution and
parallel attention while preserving subtle shape and texture variations; and
the Dynamic Pixel Attention Module, which generates pixel-wise weighting maps
to rebalance uneven foreground and background distributions and sharpen
responses to true object regions. Extensive experiments on the VisDrone
benchmark demonstrate that MGDFIS consistently outperforms state-of-the-art
methods across diverse backbone architectures and detection frameworks,
achieving superior precision and recall with low inference time. By striking an
optimal balance between accuracy and resource usage, MGDFIS provides a
practical solution for small-object detection on resource-constrained UAV
platforms.
