AM-MSFF:一种基于注意力机制和多级特征融合的害虫识别网络
Meng Zhang1, Wenzhong Yang1,2, Danny Chen1
1School of Computer Science and Technology, Xinjiang University, Urumqi 830017, China.
Entropy (Basel, Switzerland)
|May 24, 2024
概括
本研究引入了一种使用注意力机制和多尺度特征融合 (AM-MSFF) 的新害虫识别方法,以克服传统方法的局限性. AM-MSFF模型显著提高了公开数据集上的害虫识别准确度.
科学领域:
- 农业技术 农业技术
- 计算机视觉 计算机视觉 计算机视觉
- 机器学习是机器学习.
背景情况:
- 传统的害虫识别方法与各种物种,大小,形态和复杂的背景作斗争,导致准确性低.
- 由于这些固有的复杂性,现有的模型在准确识别害虫方面面临挑战.
研究的目的:
- 开发一种新的,准确的害虫识别方法,克服传统方法的局限性.
- 通过先进的深度学习技术,提高害虫识别的准确性.
主要方法:
- 提出了一种新的害虫识别方法:注意力机制和多尺度特征融合 (AM-MSFF).
- 引入了关系意识的全球关注 (RGA),用于适应性特征加权和背景干扰减少.
- 利用多尺度特征融合 (MSFF) 和通用平均聚合 (GeMP) 进行增强的特征提取.
- 实现平衡焦点损失 (BFL),以解决害虫数据集中的类不平衡问题.
主要成果:
- AM-MSFF模型在两个公共害虫数据集上实现了高精度.
- 在IP102数据集上获得了72.64%的准确性.
- 在D0数据集上实现了99.05%的准确性.
- 在害虫识别方面表现优于大多数最先进的方法.
结论:
- 拟议的AM-MSFF方法显著提高了害虫识别的准确性.
- 注意力机制和多尺度特征融合的组合对复杂的虫害识别任务是有效的.
- BFL损失函数有效地处理类不平衡,提高模型性能.
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