QDP-YOLOv5: enhanced HIC-YOLOv5 architecture for detecting small objects

Abstract

The identification of small targets in aerial imagery continues to present difficulties owing to variations in object scale and insufficient feature characterization. Current methodologies enhance detection accuracy through attention-based mechanisms or multi-level feature integration, yet frequently result in substantial computational demands. This study introduces QDP-YOLOv5, an enhanced detection architecture derived from HIC-YOLOv5, specifically designed for effective small target recognition. The proposed framework incorporates an innovative Query-Guided Deformable Pyramid (QDP) component that dynamically adjusts perceptual ranges by combining content-sensitive queries with adaptable convolution operations. Distinct from conventional deformable convolution methods, the QDP module leverages semantic queries to guide geometric transformation and spatial sampling, rather than relying solely on local feature-driven offset prediction. These QDP components are strategically embedded across multiple feature hierarchies (backbone endpoint, P4 layer, P3 layer) to strengthen multi-scale feature extraction. Furthermore, the system employs Efficient Channel Attention (ECA) to optimize channel-specific feature weighting with negligible computational burden. Comprehensive ablation experiments validate the optimality of the QDP module’s deployment strategy in the feature pyramid. Evaluation on the VisDrone2019 benchmark reveals that QDP-YOLOv5 outperforms the original HIC-YOLOv5 model by 1.73% in mAP@0.5 and 1.00% in mAP@ [0.5:0.95] metrics, while maintaining a competitive inference speed of 62.3 FPS on an NVIDIA RTX 4090. confirming the proposed method's superior performance.