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基于EfficientNet的改进轻量级方法用于识别鸟歌.

Haolun He1, Hui Luo2

  • 1College of Computer and Control Engineering, Northeast Forestry University, Harbin, China.

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|July 3, 2025
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概括
此摘要是机器生成的。

这项研究引入了一种轻量级的EfficientNet-B0模型用于鸟声识别,提高了准确度3.2%至96.04%,同时降低了参数16.4%. 这使得野外监控设备更方便地识别鸟歌.

关键词:
注意力机制注意力机制鸟类歌声识别 鸟类歌声识别有效的网络-B0轻量级的模型轻量级的模型

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科学领域:

  • 生物声学是一种生物声学.
  • 机器学习 机器学习
  • 计算机视觉 计算机视觉

背景情况:

  • 传统的鸟识别模型是计算密集型和参数重的,限制了它们在嵌入式现场设备中的使用.
  • 对于生态监测,需要高效准确的鸟识别系统.

研究的目的:

  • 开发适合嵌入式现场监控的轻量级和准确的鸟声识别模型.
  • 提高鸟声识别模型的效率和特征表达.

主要方法:

  • 开发了经过修改的EfficientNet-B0架构,结合了ECA注意力机制,以减少参数和增强特征表达.
  • 调整了MBConv结构的卷积内核,并将CBAM注意力机制集成到中间层.
  • 用亚当优化算法来加速网络的融合.

主要成果:

  • 拟议的模型在识别十种鸟类方面达到96.04%的准确性,比原始模型提高了3.2%.
  • 参数数量减少了16.4%,使该模型更适合嵌入式系统.
  • 观察到增强的特征表达和更快的融合.

结论:

  • 轻量级的EfficientNet-B0模型在鸟声识别准确性和效率方面提供了显著的改进.
  • 该模型的复杂性降低和性能提升使其非常适合在生态现场监测中的实际应用.
  • 这种方法成功地平衡了鸟歌识别的准确性和计算成本.