研究目标类别对神经网络压缩性在能源自主鸟类监测中的影响
Investigating Target Class Influence on Neural Network Compressibility for Energy-Autonomous Avian Monitoring
AI总结：
本文提出在低成本MCU上部署机器学习模型，研究目标类别数量对神经网络压缩性的影响，实现高效能源自主的鸟类监测。
摘要：
生物多样性丧失对人类构成重大威胁，使野生动物监测成为评估生态系统健康的关键。鸟类作为研究对象具有广泛 popularity 和通过其独特歌声识别的便利性。传统鸟类监测方法需要人工计数，因此成本高且效率低。在被动声学监测中，长时间记录声音景观，随后分析以识别鸟类物种。机器学习方法已在多种物种和环境中大大加快了这一过程，但现有解决方案需要复杂的模型和大量计算资源。相反，我们提出在野外直接在低成本微控制器单元（MCU）上运行机器学习模型。由于由此产生的硬件和能源限制，需要高效的AI架构。在本文中，我们介绍了在MCU上进行鸟类监测的方法。我们训练并压缩了针对不同目标类别的模型，以评估边缘设备上多种鸟类物种的检测情况，并评估物种数量对神经网络压缩性的影响。我们的结果表明，压缩率显著，性能损失极小。我们还提供了不同硬件平台的基准测试结果，并评估了部署能源自主设备的可行性。
英文摘要：
Biodiversity loss poses a significant threat to humanity, making wildlife monitoring essential for assessing ecosystem health. Avian species are ideal subjects for this due to their popularity and the ease of identifying them through their distinctive songs. Traditionalavian monitoring methods require manual counting and are therefore costly and inefficient. In passive acoustic monitoring, soundscapes are recorded over long periods of time. The recordings are analyzed to identify bird species afterwards. Machine learning methods have greatly expedited this process in a wide range of species and environments, however, existing solutions require complex models and substantial computational resources. Instead, we propose running machine learning models on inexpensive microcontroller units (MCUs) directly in the field. Due to the resulting hardware and energy constraints, efficient artificial intelligence (AI) architecture is required. In this paper, we present our method for avian monitoring on MCUs. We trained and compressed models for various numbers of target classes to assess the detection of multiple bird species on edge devices and evaluate the influence of the number of species on the compressibility of neural networks. Our results demonstrate significant compression rates with minimal performance loss. We also provide benchmarking results for different hardware platforms and evaluate the feasibility of deploying energy-autonomous devices.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
