AquaYOLO26: A Degradation-Aware YOLO26 Framework for Underwater Marine Debris Detection and Edge Deployment

Against the backdrop of escalating marine pollution, automated visual detection of submerged debris by autonomous underwater vehicles (AUVs) is essential for robotic ocean cleanup. However, robust underwater detection is fundamentally challenged by an asymmetric visual degradation process: wavelength-dependent light attenuation weakens red channel responses more severely than blue–green channels, turbidity-induced scattering disrupts the symmetry of small target features, and cross-water-domain shifts further distort feature distributions. To address these challenges, this paper proposes AquaYOLO26, a degradation-aware and domain-adaptive edge detection framework that explicitly models underwater asymmetry while restoring feature-level balance for robust perception. Specifically, we introduce Underwater-aware Batch Normalization (UWBN) to compensate for channel-asymmetric color attenuation within the feature extractor; Turbidity-conditioned Small Target-Aware Label Assignment (Turb-STAL) to asymmetrically adjust candidate assignment for obscured small targets under scattering conditions; and Domain-Adversarial Progressive Loss (DA-ProgLoss) to align feature representations across heterogeneous water environments. Experiments on the SeaClear, TrashCan 1.0, and Trash-ICRA19 benchmarks show that AquaYOLO26n achieves 73.4 ± 0.33% mAP@0.5 on the primary SeaClear test split and, in the representative run cross-domain evaluation, improves strict zero-shot transfer performance on Trash-ICRA19 by 8.9 percentage points over the YOLO26n baseline. For embedded deployment, the model retains a compact profile of 2.46 M parameters and 6.2 GFLOPs, achieving 45.1 ± 0.5 FPS on the NVIDIA Jetson Orin NX. Compared with Improved YOLOv11, AquaYOLO26n achieves a 1.1 pp higher mean mAP@0.5 while using 79.0% fewer parameters and 77.0% fewer GFLOPs and delivering higher Jetson throughput (45.1 ± 0.5 vs. 28.4 ± 0.5 FPS). These results highlight a favorable accuracy–efficiency trade-off for resource-constrained underwater robotic sensing.