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通过视听融合识别鱼类养强度的交叉模式互补性学习
Jian Li1,2, Yanan Wei2, Wenkai Ma3,4
1School of Biology and Food Engineering, Fuyang Normal University, Fuyang 236037, China.
Animals : an open access journal from MDPI
|August 14, 2025
概括
本研究引入了适应性交叉模式注意力融合网络 (ACAF-Net) 来评估鱼类养强度. 这种新的方法通过自适应地融合音频和视觉数据来提高准确性,优于单模式方法.
科学领域:
- 水产养殖技术 水产养殖技术
- 生物声学是一种生物声学.
- 计算机视觉 计算机视觉 计算机视觉
背景情况:
- 准确的鱼类养强度评估对于水产养殖效率和鱼类健康至关重要.
- 单模监控 (音频或视觉) 在复杂的水下环境中面临挑战,如,照明不良,遮蔽和噪音等.
- 现有的多模式融合方法通常使用简单的策略,未能利用互补数据或动态评估可靠性.
研究的目的:
- 开发一个先进的跨模式学习框架,用于强大的水下行为分析.
- 通过有效整合声学和视觉数据,提高鱼类养强度评估的准确性.
- 解决单模和常规聚变技术在具有挑战性的水环境中的局限性.
主要方法:
- 提出了适应性交叉模式注意力融合网络 (ACAF-Net),这是一个利用两阶段注意力融合机制的框架.
- 实现了跨模式增强阶段,使用低级别双线聚合和可学习的聚变权重.
- 开发了一种适应性注意力融合阶段,用于基于可靠性和互补性的声学和视觉特征的动态加权,包括维度对齐和注意力机制.
主要成果:
- 与单模和传统的融合方法相比,ACAF-Net表现出更高的性能.
- 在鱼类养强度评估的准确性得到了显著的6.4%的改进.
- 验证了交叉模式互补的有效性,用于分析水下行为.
结论:
- 拟议的ACAF-Net框架有效地利用交叉模式的互补性,以加强水下行为分析.
- 适应性聚变机制提高了复杂水生环境中的监测可靠性.
- 这项工作为开发智能水产养殖监测系统提供了基础.
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