Underwater fish image segmentation is the key technology to realizing intelligent fisheries and ecological monitoring. However, the problems of light attenuation, blurred boundaries, and low contrast caused by complex underwater environments seriously restrict the segmentation accuracy. In this paper, RetinalConet, an underwater fish segmentation network based on bionic retina dual-channel and multi-module cooperation, is prop...

In aquaculture, the presence of dead fish on the water surface can serve as a bioindicator of health issues or environmental stressors. To enhance the precision of detecting dead fish floating on the water’s surface, this paper proposes a detection approach that integrates data-driven insights with advanced modeling techniques. Firstly, to reduce the influence of aquatic disturbances and branches during the identification proc...

This study proposes Combining Attention and Brightness Adjustment Network (CABA-Net), a deep learning network for underwater image restoration, to address the issues of underwater image color-cast, low brightness, and low contrast. The proposed approach achieves a multi-branch ambient light estimation by extracting the features of different levels of underwater images to achieve accurate estimates of the ambient light. Additio...