GLoDA-Net: A lightweight global-local directional aggregation network for floating waste detection in inland waters...

Floating bottle waste detection in inland water environments remains challenging because targets are often small, low-contrast, elongated, partially submerged, and easily confused with water-surface reflections, ripples, vegetation, and bank-side textures. To address these challenges, this study proposes GLoDA-Net, a lightweight YOLOv12n-based detector for USV-oriented floating debris monitoring. The network combines compact global-local feature extraction, contextual interaction, and direction-aware feature fusion through RepViT, C2PSAMILA, and APC2f. Experiments on the FLOW-img benchmark show that GLoDA-Net achieves 88.0% Precision, 80.9% Recall, and 87.7% mAP@50, outperforming the original YOLOv12n by 5.1, 1.7, and 4.8 percentage points, respectively. The model maintains a compact structure with 2.59M parameters and a size of 5.6 MB, while reaching 117 FPS on an RTX 4090 GPU. Ablation, robustness, cross-scenario, multi-platform inference-speed, and cross-dataset evaluations further demonstrate improvements in detection accuracy, robustness, transferability, and computational efficiency. The measured speeds support real-time inference on the evaluated GPU platforms and indicate deployment potential for USV-based floating bottle monitoring, while resource-constrained onboard systems may require further optimization.