FastUNet: : Fast hierarchical multi-patch underwater enhancement network for industrial recirculating aquaculture

The industrialized aquaculture with recirculating water systems has gained increasing attention and rapid development in recent years. However, the existing underwater enhancement methods are time-consuming and severely hinder their online application in industrialized aquaculture due to the requirement for real-time processing. We found that their main limitations are the insufficient utilization of the hierarchical network approach and valuable prior information in underwater scenarios. To address this problem, we propose a novel architecture called FastUNet and introduce a visual enhancement benchmark dataset specifically designed for industrialized aquaculture with recirculating water systems. Surprisingly, by learning the fast multi-patch hierarchical module and employing prior constraints including color loss and homology loss, FastUNet achieves comparable or even better results than many deep learning methods. In terms of runtime, it is up to 30 times faster than current underwater enhancement methods. The processing time for images with a resolution of 5474 × 3653 is only 0.137s. The real application scenario of factory recirculating aquaculture is considered by FastUNet, and the embedding of prior information under the fast network framework is explored. In this paper, 14 state-of-the-art underwater enhancement methods are compared, and excellent results are obtained on 2 Full-reference datasets and 2 No-referenced datasets. The application test results of SIFT significant point detection, geometric rotation, edge detection, target segmentation and target detection were also presented, which strongly verified the feasibility of FastUNet in the field of factory recirculating aquaculture.