YOLO-Vortex：基于漩涡聚合网络的水下目标检测模型
YOLO-Vortex: Underwater Target Detection Model Based on Vortex Aggregation Network

水下目标检测在海洋探索、生态保护和水下机器人导航等领域具有重要应用。然而，由于水下环境的复杂性，如光照不均匀、悬浮颗粒干扰和低对比度图像，传统的目标检测方法在水下环境中的表现往往不尽如人意，尤其是面对数据中的噪声问题。为了解决这一问题，本研究提出了一种基于YOLOv7的改进模型用于水下目标检测。我们将YOLOv7作为基线模型，针对其在水下环境中的不足之处，对模型的关键模块进行了优化。具体而言，我们提出了一种漩涡聚合网络模块来破坏噪声数据，并在此过程前引入了空间注意力机制，帮助网络更好地关注重要特征，并抑制不相关的噪声；针对下采样过程中可能存在的信息丢失问题，我们提出了空间到深度池化模块(STD-MP)，通过将空间特征转换为深度特征，结合最大池化操作完成下采样过程；最后，我们对损失函数进行了优化。实验结果表明，我们的模型相比于基准模型提升了4.2%的mAP。
Underwater object detection has important applications in fields such as ocean exploration, ecological protection, and underwater robotics navigation. However, due to the complexity of the underwater environment, including uneven lighting, interference from suspended particles, and low-contrast images, traditional object detection methods often perform suboptimally in underwater scenarios, particularly when dealing with noisy data. To address this issue, this study proposes an improved model based on YOLOv7 for underwater object detection. We use YOLOv7 as the baseline model and optimize its key modules to overcome its limitations in underwater environments. Specifically, we introduce a vortex aggregation network module to disrupt noisy data, incorporating a spatial attention mechanism before this process to help the network better focus on important features and suppress irrelevant noise. To tackle the issue of potential information loss during downsampling, we propose the Space-To-Depth Pooling (STD-MP) module, which converts spatial features into depth features and combines them with max pooling for downsampling. Finally, we optimize the loss function. Experimental results show that our model achieves a 4.2% improvement in mAP compared to the baseline model.