5G AI-IoT 系统用于鸟类监测和歌曲分类.
Jaume Segura-Garcia1, Sean Sturley2, Miguel Arevalillo-Herraez1
1Escola Tecnica Superior d'Enginyeria, Universitat de Valencia, 46100 Burjassot, Spain.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
机器学习和深度学习提升了鸟类歌声识别. 新的深度卷积神经网络 (DCNNs) 为边缘设备提供高效的鸟类识别,平衡精度和大小.
科学领域:
- 鸟类学 鸟类学是一门学科.
- 生态生态学 生态生态学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 准确的动物物种识别对于生物和生态研究至关重要,包括鸟类保护和生态系统健康评估.
- 机器学习 (ML) 和深度学习 (DL) 显著提高了鸟类歌声识别能力.
- 人工智能-物联网 (AI-IoT) 方法利用音频数据进行鸟类识别.
研究的目的:
- 开发和评估AI-IoT系统,使用音频记录识别鸟类.
- 为了比较不同深层卷积神经网络 (CNN) 的性能,用于鸟歌的分类.
- 设计能效的DCNN模型,适合在小型,资源有限的设备上部署,如单板计算机 (SBC) 和微控制器 (MCU).
主要方法:
- 利用5G物联网系统收集原始音频数据.
- 采用使用CNN (EfficientNet,MobileNet) 训练的图像特征比较技术,在鸟歌谱图上使用ImageNet权重.
- 开发并测试了两种定制深度CNN (DCNN) 用于鸟声分类,重点关注参数减少和效率.
主要成果:
- ImageNet加权的CNN在识别大多数鸟类物种中达到高达75%的准确性.
- 这些标准的CNN具有大量的参数,因此在推断过程中能效较低.
- 定制设计的DCNN成功地减少了模型大小,同时保持了具有竞争力的精度水平.
结论:
- 深度学习模型,特别是定制的DCNN,显示出对准确和高效的鸟识别的重大前景.
- 开发的DCNN适合集成到边缘计算设备 (SBC,MCU) 中,以实时监控鸟类.
- 这项研究有助于推进人工智能在鸟类学和生态监测中的应用.
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