Research on an underwater image segmentation algorithm based on YOLOv8

Abstract

Underwater image segmentation faces complex problems such as target type, number, scale, and environmental interference. The traditional segmentation method relies on much manual annotation and expertise, extracts only the surface information of the image, and has low segmentation accuracy and efficiency, so the image segmentation method based on deep learning is chosen. Deep learning algorithms are used on top of the YOLOv8, and the adaptive self-supervised learning (Convnext V2) module, the lightweight network (Slim Neck), and the dynamic sparse attention mechanism (bilevel routing attention, Biformer) are added, which improves the YOLOv8 algorithm for single-stage instance segmentation of different underwater targets. The Convnext V2 module introduces the global response normalization (GRN) unit, which enhances the feature competition and reduces the model computation. A slim neck network is designed, which achieves a double improvement in model inference speed and accuracy by the operation of dense convolution in the lightweight convolution (GSConv) module and the design of a cross-level network structure (VoV-GSCSP). The introduction of Biformer’s dynamic sparse attention mechanism achieves more flexible computational allocation and content awareness. Comparison experiments show that the improved algorithm dramatically increases the segmentation speed while improving the segmentation accuracy. On the dual-target dataset, the mAP is improved by 3.5% from 0.753 to 0.779, and the FPS is improved by 86% from 63 to 117. The experiments validate that the improved algorithm can be used for the segmentation of different underwater images.