DGQ-YOLO: Depth-Guided Quality-Aware detection with Pseudo-Depth for aerial imagery
Xinxing Hou, Wenlin Liu · Journal of King Saud University - Computer and Information Sciences · 2026
Small, densely distributed, and occluded objects make UAV-view detection difficult because RGB-only detectors represent scene geometry only implicitly. Monocular pseudo-depth provides geometric cues without an additional sensor, but its derivation from RGB introduces local errors and estimator-dependent variation. We propose DGQ-YOLO, an RGB-dominant detector that introduces pseudo-depth at the backbone and neck. The Depth-Quality-Aware Adapter (DQAdapter) applies depth-conditioned spatial and channel gates through a zero-initialized residual path. The Consistency-Aware Geometric Feature Pyramid Network (CAGFPN) uses explicit RGB–depth discrepancy features to refine multi-scale representations. Across three random seeds on pseudo-depth-augmented VisDrone2019-DET, DGQ-YOLO-n improves test-set $$\textrm{mAP}_{50}$$ mAP 50 from 25.54% to 27.89% and $$\textrm{mAP}_{50:95}$$ mAP 50 : 95 from 14.07% to 15.64%. Controlled perturbation and gate-intervention experiments support reliability-sensitive modulation of the depth contribution. After dataset-specific retraining on UAVDT, DGQ-YOLO-n gains 2.14 $$\textrm{mAP}_{50:95}$$ mAP 50 : 95 points over its RGB baseline. The added modules increase parameter counts by approximately 3% and GFLOPs by less than 10%. DGQ-YOLO therefore improves pseudo-depth-assisted aerial detection while retaining detector-level efficiency, although online throughput remains constrained by pseudo-depth generation.