FiVOS: A Fish Segmentation Algorithm Based on Interactive Video Object Segmentation and Filter Enhancement

2026-10-01

AI Summary

This study addresses the issues of error accumulation and mask loss in fish segmentation within aquaculture videos, which arise from high intra-class similarity. To tackle these challenges, an improved interactive video object segmentation algorithm is proposed. Methodologically, a sequential noise filter is designed to eliminate erroneous mask propagation, and a novel mask block filter is introduced to detect and correct early-stage errors. Furthermore, two domain-specific fish datasets are constructed to alleviate the bottleneck of data scarcity. Experimental results demonstrate that the proposed method achieves state-of-the-art performance on fish video segmentation tasks, providing reliable technical support for precision fisheries management.

Abstract

With the continuous expansion of aquaculture, precise and efficient monitoring of fish behavior has become increasingly critical for improving farming efficiency and reducing economic losses. In particular, with the ongoing enhancement of computational capabilities in deep learning models, vision-based fish segmentation methods are garnering growing attention. By analyzing video segmentation results, fish behavior can be effectively tracked, thereby providing reliable data support for the precise regulation of aquaculture environments. However, existing deep learning-based video segmentation methods for aquaculture scenarios often overlook the dynamic correlations between video frames. In contrast, Interactive Video Object Segmentation (IVOS) employs an interaction-propagation scheme to achieve high-precision segmentation while minimizing user effort, thereby enhancing monitoring efficiency. Yet, IVOS applications in aquaculture remain limited due to data scarcity, and are susceptible to error accumulation and mask loss over long sequence propagation due to high intra-class similarity. In response, this paper proposes an improved interactive video object segmentation method (FiVOS) and constructs two fish-specific datasets. FiVOS utilizes a mask block filter to enable early detection and correction of erroneous propagated mask blocks, enhancing filtering accuracy through a rule-based thresholding approach. Additionally, it serializes noise filters to further eliminate erroneous mask noise, thereby improving model robustness. Experimental results demonstrate that FiVOS achieves state-of-the-art (SOTA) performance in fish video segmentation tasks, providing robust technical support for fish behavior research.

Problem

• Fish Segmentation
• Interactive Video Object Segmentation
• Aquaculture
• Error Accumulation
• Mask Loss

Innovation

• Interactive Video Object Segmentation
• Fish Segmentation
• Mask Block Filter
• Noise Filter Serialization
• Aquaculture