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.

翻译：本研究提出ADOD，一种解决水下目标检测中领域泛化问题的新方法。该方法增强了模型在不同及未见领域间的泛化能力，确保在多样化水下环境中的鲁棒性。第一个关键贡献是残差注意力YOLOv3，这是由残差注意力模块驱动的YOLOv3框架的新型变体。这些模块使模型能够聚焦于信息特征同时抑制背景噪声，进而提升检测精度和跨领域适应性。第二个贡献是基于注意力的领域分类模块，该模块在训练过程中至关重要。它能帮助模型识别领域特定信息，促进领域不变特征的学习。因此，ADOD能够有效泛化至具有不同视觉特征的水下环境。在多个水下数据集上的广泛实验表明，与最先进的领域泛化方法相比，ADOD在具有挑战性的场景中表现出更优性能。所提模型在已知和未知领域均实现了卓越的检测性能，展示了其处理水下目标检测任务中领域偏移的有效性。ADOD代表了自适应目标检测领域的重大进展，为水下环境中的实际应用提供了有前景的解决方案。鉴于此类场景中领域偏移的普遍性，该模型的强泛化能力将成为实际水下监控与海洋研究工作中的宝贵工具。