MaskGuide: Efficient Distillation for Deployable Lightweight Segmentation in Marine Environments

Abstract

The growing demand for efficient image segmentation in marine ecological studies is currently constrained by two key factors: the high computational requirements of models such as the Segment Anything Model (SAM) and the degraded accuracy of lightweight models in underwater environments. To overcome these challenges, we introduce a Three-stage Iterative Optimization (TIO) training framework and a decoupled knowledge distillation strategy, termed MaskGuide, both of which facilitate the development of our optimized Tiny-MSAM model on the edge device. This model significantly improves frame processing speed while maintaining high accuracy, achieving an optimal tradeoff between segmentation precision and inference speed in practical underwater and marine applications. Our research provides a feasible direction for deploying advanced segmentation models in marine and other resource-constrained scenarios. The Tiny-MSAM model, even when trained from scratch, contains only 0.5% of the parameters of the SAM model and 58% of those in MobileSAM. In existing underwater image segmentation benchmarks (e.g., UIIS dataset), it outperforms MobileSAM by a large margin and reaches 99.4% of the performance of the SAM ViT-H variant.