CIS: A Coral Instance Segmentation Network Model with Novel Upsampling, Downsampling, and Fusion Attention Mechanism

Abstract

Coral segmentation poses unique challenges due to its irregular morphology and camouflage-like characteristics. These factors often result in low precision, large model parameters, and poor real-time performance. To address these issues, this paper proposes a novel coral instance segmentation (CIS) network model. Initially, we designed a novel downsampling module, ADown_HWD, which operates at multiple resolution levels to extract image features, thereby preserving crucial information about coral edges and textures. Subsequently, we integrated the bi-level routing attention (BRA) mechanism into the C2f module to form the C2f_BRA module within the neck network. This module effectively removes redundant information, enhancing the ability to distinguish coral features and reducing computational redundancy. Finally, dynamic upsampling, Dysample, was introduced into the CIS to better retain the rich semantic and key feature information of corals. Validation on our self-built dataset demonstrated that the CIS network model significantly outperforms the baseline YOLOv8n model, with improvements of 6.3% and 10.5% in PB and PM and 2.3% and 2.4% in mAP50B and mAP50M, respectively. Furthermore, the reduction in model parameters by 10.1% correlates with a notable 10.7% increase in frames per second (FPS) to 178.6, thus effectively meeting real-time operational requirements.

Keywords

• deep learning
• coral reef monitoring
• attention mechanism
• instance segmentation
• neural network