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Updated: Jun 10, 2025

Flying Insect Detection and Classification with Inexpensive Sensors
Published on: October 15, 2014
卷积神经网络实时分类蜂巢声学模式在受约束的设备上
Antonio Robles-Guerrero1, Salvador Gómez-Jiménez1, Tonatiuh Saucedo-Anaya2
1Unidad Académica de Ingeniería, Universidad Autónoma de Zacatecas, Zacatecas 98000, Mexico.
卷积神经网络 (CNNs) 显示出使用声学监测蜜蜂健康的前景. 这项研究评估了受约束硬件上的CNN,为高效的蜜蜂养殖监测系统找到合适的架构.
科学领域:
- 农业技术 农业技术
- 计算生物学 计算生物学
- 机器学习 机器学习
背景情况:
- 卷积神经网络 (CNN) 有效地对蜂群声学模式进行健康评估.
- 与传统方法相比,实施用于蜜蜂监测的CNN可能会导致较高的计算成本和能源需求.
研究的目的:
- 调查CNN架构的可行性,以在受约束的硬件上开发蜂群监控系统.
- 分析各种CNN模型和单板计算机上的声学数据持续时间的性能权衡.
主要方法:
- 在Nvidia Jetson Nano,Raspberry Pi 5和Orange Pi 5单板计算机上测试了十个CNN架构.
- 模型被训练在不同持续时间 (1-30秒) 的声谱图上,通过Optuna和k-fold交叉验证进行超参数优化.
- 推断时间和功耗被测量用于性能比较.
主要成果:
- 在CNN架构和单板计算机中,性能各不相同,声学样本持续时间较短影响了结果.
- 具体的CNN模型展示了在测试设备的功率和计算限制范围内高效运行的潜力.
结论:
- 在资源有限的设备上,CNN可以被调整为有效的蜜蜂健康监测系统.
- 这项研究为开发使用先进人工智能的实用,低功耗蜜蜂养殖监测解决方案提供了基础.
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