In this paper, we present a novel Amplitude-Modulated Stochastic Perturbation and Vortex Convolutional Network, AMSP-UOD, designed for underwater object detection. AMSP-UOD specifically addresses the impact of non-ideal imaging factors on detection accuracy in complex underwater environments. To mitigate the influence of noise on object detection performance, we propose AMSP Vortex Convolution (AMSP-VConv) to disrupt the noise distribution, enhance feature extraction capabilities, effectively reduce parameters, and improve network robustness. We design the Feature Association Decoupling Cross Stage Partial (FAD-CSP) module, which strengthens the association of long and short range features, improving the network performance in complex underwater environments. Additionally, our sophisticated post-processing method, based on Non-Maximum Suppression (NMS) with aspect-ratio similarity thresholds, optimizes detection in dense scenes, such as waterweed and schools of fish, improving object detection accuracy. Extensive experiments on the URPC and RUOD datasets demonstrate that our method outperforms existing state-of-the-art methods in terms of accuracy and noise immunity. AMSP-UOD proposes an innovative solution with the potential for real-world applications. Our code is available at https://github.com/zhoujingchun03/AMSP-UOD.

翻译：本文提出了一种新颖的幅值调制随机扰动与涡旋卷积网络（AMSP-UOD），专为水下目标检测设计。AMSP-UOD 特别解决了复杂水下环境中非理想成像因素对检测精度的影响。为减轻噪声对目标检测性能的干扰，我们提出了AMSP涡旋卷积（AMSP-VConv）模块，用于扰乱噪声分布、增强特征提取能力、有效减少参数并提升网络鲁棒性。我们设计了特征关联解耦跨阶段部分连接（FAD-CSP）模块，强化了长短距离特征的关联性，从而提升了网络在复杂水下环境中的性能。此外，我们基于非极大值抑制（NMS）并结合纵横比相似度阈值的高级后处理方法，优化了密集场景（如水草与鱼群）中的检测效果，提高了目标检测精度。在URPC和RUOD数据集上的大量实验表明，我们的方法在精度和抗噪性方面优于现有最先进方法。AMSP-UOD提出了一种创新性的解决方案，具备实际应用潜力。我们的代码公开于https://github.com/zhoujingchun03/AMSP-UOD。