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FFM-ViT:一种基于深度特征和变压器的高效鱼类分类方法
Yuwei Gao1, Xiaoyong Li1, Jian Xiang1
1Zhejiang University of Science and Technology, School of Information and Electronic Engineering, Hangzhou, China.
Journal of fish biology
|October 1, 2025
概括
一个新的深度学习模型,特征融合模块视觉转换器 (FFM-ViT),显著提高了鱼类物种识别的准确性. 这种方法增强了特征提取,以改善渔业管理和生物多样性保护.
科学领域:
- 海洋生物学 海洋生物学
- 计算机科学 计算机科学
- 人工智能的人工智能是人工智能.
背景情况:
- 准确的鱼类物种识别对于渔业管理和生物多样性保护至关重要.
- 目前的分类方法与小数据集和高物种相似性作斗争.
- 限制需要先进的计算方法来有效识别鱼类.
研究的目的:
- 引入一种新的深度学习模型,即特征融合模块视觉转换器 (FFM-ViT),用于增强鱼类物种分类.
- 为了应对有限数据和现有鱼类识别方法的高度相似性的挑战.
- 为了提高生态监测的鱼类分类的准确性和效率.
主要方法:
- 通过整合移动反向瓶卷积 (MBConv) 和融合移动反向瓶卷积 (Fuse-MBConv) 块,开发了功能融合模块视觉变压器 (FFM-ViT).
- 整合了通道空间融合注意 (CSMA) 模块,以促进特征提取和通道融合.
- 创建并使用Oceanfish78数据集,包括78种鱼类,用于模型培训和验证.
主要成果:
- 在Oceanfish78数据集上,FFM-ViT模型实现了90.2%的准确率,明显优于标准视觉变压器 (ViT) 模型 (80.4%).
- 对fish4knowledge和Fish31数据集的比较分析表明,与shufflenet,convnext和swin变压器等模型相比,它们的性能优越.
- 经验结果证实了FFM-ViT在鱼类分类任务中的有效性.
结论:
- FFM-ViT模型为鱼类物种识别提供了强大而有效的解决方案,特别是在数据有限的具有挑战性的场景中.
- 拟议的方法增强了高维信息提取和特征融合,推进了鱼类学中的深度学习应用.
- 在渔业以外的各种环境环境中,FFM-ViT为近似目标识别提供了有价值的见解.
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