  This research presents ADOD, a novel approach to address domain
generalization in underwater object detection. Our method enhances the model's
ability to generalize across diverse and unseen domains, ensuring robustness in
various underwater environments. The first key contribution is Residual
Attention YOLOv3, a novel variant of the YOLOv3 framework empowered by residual
attention modules. These modules enable the model to focus on informative
features while suppressing background noise, leading to improved detection
accuracy and adaptability to different domains. The second contribution is the
attention-based domain classification module, vital during training. This
module helps the model identify domain-specific information, facilitating the
learning of domain-invariant features. Consequently, ADOD can generalize
effectively to underwater environments with distinct visual characteristics.
Extensive experiments on diverse underwater datasets demonstrate ADOD's
superior performance compared to state-of-the-art domain generalization
methods, particularly in challenging scenarios. The proposed model achieves
exceptional detection performance in both seen and unseen domains, showcasing
its effectiveness in handling domain shifts in underwater object detection
tasks. ADOD represents a significant advancement in adaptive object detection,
providing a promising solution for real-world applications in underwater
environments. With the prevalence of domain shifts in such settings, the
model's strong generalization ability becomes a valuable asset for practical
underwater surveillance and marine research endeavors.
