Physics-Coupled Frequency Dynamic Adaptation Network for Domain Generalized Underwater Object Detection

Despite technological progress in underwater object detection, there are still problems such as the domain shift stemming from diverse environmental conditions and severe image degradation in underwater environments. To address these challenges, we propose a hypernetwork-powered domain generalization framework that synergizes physical priors with multi-frequency feature learning, called Hy-UOD. To combat domain shift challenges, we devise a meta-learning empowered hypernetwork architecture that synthesizes domain-generalization parameters through environment-specific physical descriptor encoding for cross-domains. To further mitigate the impact of complex degradation on object detection performance, we designed a Multi-frequency Feature Dynamic Adaptation (i.e., MFDA) module based on hypernetwork features and domain-specific information. This module implements a systematic compensation for degraded features through a multi-level dynamic adaptation mechanism: ''low-frequency correction, high-frequency refinement, and mid-frequency reconstruction''. Experiments on multiple underwater datasets demonstrate the robust detection performance and strong cross-domain generalization capability of our method. The source code will be available at https://github.com/White-cat-ed/HyUOD.