In recent years, underwater object detection (UOD) has become a prominent research area in the computer vision community. However, existing UOD approaches are still vulnerable to underwater environments, which mainly include light scattering and color shifting. The blurring problem caused by water scattering on underwater images makes the high-frequency texture edge less obvious, affecting the detection effect of objects in the image. To address this issue, we design a multi-scale high-frequency information enhancement module to enhance the high frequency features extracted by the backbone network and improve the detection effect of the network on underwater objects. Another common issue caused by scattering and color shifting is that it can easily change the low-frequency information in the background of underwater images, leading to performance degradation of the same target in different underwater scenes. Therefore, we have also designed a multi-scale gated channel information optimization module to reduce the scattering and color shifting effects on the channel information of underwater images and adaptively compensate the features for different underwater scenes. We tested the detection performance of our designed method on three typical underwater object detection datasets, RUOD, UDD and UODD. The experimental results proved that our method performed better than existing detection methods on underwater object detection datasets.

中文翻译：

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在频率级别进行水下物体检测的改进功能


近年来，水下物体检测 （UOD） 已成为计算机视觉界的一个重要研究领域。然而，现有的 UOD 方法仍然容易受到水下环境的影响，主要包括光散射和颜色偏移。水下图像上水散射导致的模糊问题，使得高频纹理边缘不太明显，影响了对图像中目标的检测效果。针对这一问题，我们设计了一种多尺度高频信息增强模块，以增强骨干网络提取的高频特征，提高网络对水下目标的检测效果。散射和色偏导致的另一个常见问题是，它很容易改变水下图像背景中的低频信息，导致同一目标在不同水下场景中的性能下降。因此，我们还设计了多尺度门控通道信息优化模块，以减少对水下图像通道信息的散射和移色影响，并针对不同的水下场景自适应补偿特征。我们在三个典型的水下物体检测数据集 RUOD 、 UDD 和 UODD 上测试了我们设计的方法的检测性能。实验结果证明，我们的方法在水下目标检测数据集上的表现优于现有的检测方法。