  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.
