AlgaeMask: An Instance Segmentation Network for Floating Algae Detection

Abstract

Video surveillance on the offshore booster station and around the coast is a effective way to monitor floating macroalgae. Previous studies on floating algae detection are mainly based on traditional image segmentation methods. However, these algorithms cannot effectively solve the problem of extracting Ulva prolifra and Sargassum at different sizes and views. Recently, instance segmentation methods have achieved great success in computer vision applications. In this paper, based on the CenterMask network, a novel instance segmentation architecture named AlgaeMask is proposed for floating algae detection from the surveillance videos. To address the feature extraction ability of the network in the inter-dependencies for position and channel, we introduce a new OSA-V3 module with the dual-attention block, which consists of a position attention mechanism and channel attention mechanism. Meanwhile, scale-equalizing pyramid convolution is introduced to solve the problem of scale difference. Finally, we introduce the feature decoder module based on FCOS head and segmentation head to obtain the segmentation area of floating algae in each bounding box. The extensive experiment results show that the average precision of our AlgaeMask in the tasks of mask segmentation and box detection can reach 44.22% and 48.13%, respectively, which has 15.09% and 8.24% improvement over CenterMask. In addition, the AlgaeMask can meet the real-time requirements of floating algae detection.