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 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. Code will be made publicly available.

本文提出了一种针对水下物体检测的新颖振幅调制随机扰动和涡旋卷积网络（AMSP-UOD）。AMSP-UOD通过AMSP涡旋卷积（AMSP-VConv）来减弱噪声的影响，增强特征提取能力，减少参数并提高网络的鲁棒性。通过设计长程和短程特征的关联增强模块（FAD-CSP），改善了复杂水下环境中的网络性能。此外，基于非最大值抑制和纵横比相似性阈值的后期处理方法优化了密集场景中的检测，提高了物体检测的准确性。大量在URPC和RUOD数据集上的实验表明，该方法在准确性和抗噪性方面优于现有的最先进方法。AMSP-UOD提出了一种具有实际应用潜力的创新解决方案。代码将公开提供。

AMSP-UOD: 涡旋卷积与随机扰动相遇的水下目标检测