High-robustness underwater vortex beam recognition using conjugate superimposed OAM modes and a deep residual network

Underwater optical vortex communication faces critical challenges from scattering, turbulence, and transient occlusions, which severely distort orbital angular momentum (OAM) modes. We propose and experimentally demonstrate a robust mode recognition scheme that combines conjugate superimposed OAM beams with a deep residual network (ResNet-50). Nine distinct underwater disturbance environments are quantitatively emulated by independently tuning kaolin concentration, water pump power, and introducing random rectangular occlusions. The petal-like intensity patterns of conjugate superimposed beams preserve discriminative structural information even under strong combined perturbations. Using power-law transformed images as input, ResNet-50 achieves ∼100% classification accuracy for 16 OAM modes across all tested disturbance levels and maintains reliable recognition when occlusions cover up to half of the beam cross-section. The inherent spatial redundancy of conjugate superposition, together with the residual network's feature preservation capability, enables near-perfect generalization without overfitting to experimental artifacts. This work provides a practical, intelligent demodulation framework for deploying highly robust underwater OAM communication systems in real-sea scenarios.