A High-Quality Instance-Segmentation Network for Floating-Algae Detection Using RGB Images

Floating-algae detection plays an important role in marine-pollution monitoring. The surveillance cameras on ships and shores provide a powerful way of monitoring floating macroalgae. However, the previous methods cannot effectively solve the challenging problem of detecting Ulva prolifera and Sargassum, due to many factors, such as strong interference with the marine environment and the drastic change of scale. Recently, the instance-segmentation methods based on deep learning have been successfully applied to many image-recognition tasks. In this paper, a novel instance-segmentation network named AlgaeFiner is proposed for high-quality floating-algae detection using RGB images from surveillance cameras. For improving the robustness of the model in complex ocean scenes, the CA-ResNet is firstly proposed by integrating coordinate attention into the ResNet structure to model both the channel- and position-dependencies. Meanwhile, the Ms-BiFPN is proposed by embedding the multi-scale module into the architecture of BiFPN to strengthen the ability of feature fusion at different levels. To improve the quality of floating-algae segmentation, the Mask Transfiner network is introduced into the AlgaeFiner to obtain the high-quality segmentation results. Experimental results demonstrate that the AlgaeFiner can achieve better performance on floating-algae segmentation than other state-of-the-art instance-segmentation methods, and has high application-value in the field of floating-macroalgae monitoring.