发表机构
China Agricultural University（中国农业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对水产养殖视频分割忽视帧间动态关联及IVOS在鱼类场景中数据稀缺、误差累积的问题，提出改进的交互式视频分割方法FiVOS，结合掩码块过滤与噪声过滤增强，构建两个鱼类数据集，实现SOTA性能。
AI 中文摘要
随着水产养殖业的持续扩张，对鱼类行为进行精确高效的监测对于提高养殖效率和减少经济损失变得日益关键。特别是随着深度学习模型计算能力的不断提升，基于视觉的鱼类分割方法正受到越来越多的关注。通过分析视频分割结果，可以有效追踪鱼类行为，从而为水产养殖环境的精准调控提供可靠的数据支持。然而，现有的面向水产养殖场景的基于深度学习的视频分割方法往往忽视了视频帧之间的动态相关性。相比之下，交互式视频目标分割（IVOS）采用交互-传播方案，在最小化用户工作量的同时实现高精度分割，从而提升监测效率。然而，由于数据稀缺，IVOS在水产养殖中的应用仍然有限，并且由于类内相似度高，在长序列传播过程中容易受到错误累积和掩码丢失的影响。为此，本文提出了一种改进的交互式视频目标分割方法（FiVOS），并构建了两个鱼类专用数据集。FiVOS利用掩码块过滤器实现对错误传播掩码块的早期检测与修正，并通过基于规则的阈值方法增强过滤精度。此外，它串联了噪声过滤器以进一步消除错误的掩码噪声，从而提高模型的鲁棒性。实验结果表明，FiVOS在鱼类视频分割任务中达到了最先进的（SOTA）性能，为鱼类行为研究提供了有力的技术支持。
英文摘要
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.
Journal refComput. Electron. Agric. 237 (2025) 110438
DOI：10.1016/j.compag.2025.110438
