Refining features for underwater object detection at the frequency level

Abstract

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.