Honeysuckle Pest Detection with a Pyramid Attention Network for Multi-Dimensional Feature Fusion

With the rapid development of precision agriculture, the application of computer vision in the intelligent detection of crop pests has advanced significantly. Within the honeysuckle cultivation industry, accurate pest identification is crucial for ensuring the quality of the medicinal material. However, existing detection models often underperform in two key scenarios: firstly, when pests exhibit substantial scale variations in images due to different shooting distances, and secondly, when distinguishing between morphologically similar pests based on subtle features. To address these challenges, this paper proposes a symmetry-aware model termed the YOLO-Multi-dimensional Pyramid Attention Module (YOLO-MPAM), which incorporates structural symmetry principles as its fundamental design paradigm. The core component, a Multi-dimensional Pyramid Attention Module (MPAM), employs a symmetrical pyramid architecture to achieve balanced feature processing across different scales. This design ensures scale symmetry by treating features at different resolutions equivalently, while the attention mechanism establishes channel–spatial symmetry through coordinated processing of both dimensions. The symmetrical design enables the reliable capture of discriminative features across objects of varying sizes, effectively addressing the inherent asymmetries in pest distribution and scale variation.