用边缘设备实时识别鸟类，低成本支持生物多样性监测。
Zwitscherkasten -- DIY Audiovisual bird monitoring
- 在低功耗硬件上部署音视频分类模型，实现边端推理。
- 声学活动检测降低能耗，视觉识别采用细粒度检测与分类。
- 适合生态监测和公众科学项目，可大规模部署。
本文提出Zwitscherkasten，一种基于音频与视觉数据的自研多模态鸟类监测系统，可在资源受限的边缘设备上运行。通过在嵌入式平台部署深度学习模型，实现生物声学与图像分类的实时处理，支持非侵入式监测。声学活动检测模块有效降低能耗，视觉识别则采用细粒度检测与分类流程。实验表明，在嵌入式平台上可实现高精度鸟类物种识别，适用于大规模生物多样性监测及公众科学应用。
原文摘要 · Abstract (English)
This paper presents Zwitscherkasten, a DiY, multimodal system for bird species monitoring using audio and visual data on edge devices. Deep learning models for bioacoustic and image-based classification are deployed on resource-constrained hardware, enabling real-time, non-invasive monitoring. An acoustic activity detector reduces energy consumption, while visual recognition is performed using fine-grained detection and classification pipelines. Results show that accurate bird species identification is feasible on embedded platforms, supporting scalable biodiversity monitoring and citizen science applications.
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