HieraEdgeNet: A Multi-Scale Edge-Enhanced Framework for Automated Pollen Recognition
Journal:
arXiv
Published Date:
Jun 9, 2025
Abstract
Automated pollen recognition is vital to paleoclimatology, biodiversity
monitoring, and public health, yet conventional methods are hampered by
inefficiency and subjectivity. Existing deep learning models often struggle to
achieve the requisite localization accuracy for microscopic targets like
pollen, which are characterized by their minute size, indistinct edges, and
complex backgrounds. To overcome this limitation, we introduce HieraEdgeNet, a
multi-scale edge-enhancement framework. The framework's core innovation is the
introduction of three synergistic modules: the Hierarchical Edge Module (HEM),
which explicitly extracts a multi-scale pyramid of edge features that
corresponds to the semantic hierarchy at early network stages; the Synergistic
Edge Fusion (SEF) module, for deeply fusing these edge priors with semantic
information at each respective scale; and the Cross Stage Partial Omni-Kernel
Module (CSPOKM), which maximally refines the most detail-rich feature layers
using an Omni-Kernel operator - comprising anisotropic large-kernel
convolutions and mixed-domain attention - all within a computationally
efficient Cross-Stage Partial (CSP) framework. On a large-scale dataset
comprising 120 pollen classes, HieraEdgeNet achieves a mean Average Precision
([email protected]) of 0.9501, significantly outperforming state-of-the-art baseline
models such as YOLOv12n and RT-DETR. Furthermore, qualitative analysis confirms
that our approach generates feature representations that are more precisely
focused on object boundaries. By systematically integrating edge information,
HieraEdgeNet provides a robust and powerful solution for high-precision,
high-efficiency automated detection of microscopic objects.
