用微型AI在边缘设备上实时识别牲畜行为，省电又快。
A Multicore and Edge TPU-Accelerated Multimodal TinyML System for Livestock Behavior Recognition
- 融合加速度计与视觉数据，用微型AI在微控制器上运行多模态模型。
- 模型体积缩小270倍，响应延迟低于80毫秒，性能不输主流方法。
- 适合偏远农场部署，支持低网速环境，可灵活扩展至多种养殖场景。
技术进步推动农业从人工劳作转向智能化管理。本文提出一种新型牲畜行为识别与运动追踪系统，基于微型机器学习（TinyML）技术、无线通信框架和微控制器平台，构建高效低成本的畜牧感知系统。系统采集并融合加速度计数据与视觉输入，建立多模态网络，完成图像分类、目标检测与行为识别三项任务。该系统在商用微控制器上实现嵌入式实时推理，模型大小最多减少270倍，响应延迟低于80毫秒，性能与现有方法相当。无线通信技术实现设备间无缝数据传输，适用于网络条件差的偏远地区。本工作提供了一种鲁棒、可扩展的物联网-边缘畜牧监控方案，可适应多样化的养殖需求，并具备良好的未来拓展性。
原文摘要 · Abstract (English)
The advancement of technology has revolutionized the agricultural industry, transitioning it from labor-intensive farming practices to automated, AI-powered management systems. In recent years, more intelligent livestock monitoring solutions have been proposed to enhance farming efficiency and productivity. This work presents a novel approach to animal activity recognition and movement tracking, leveraging tiny machine learning (TinyML) techniques, wireless communication framework, and microcontroller platforms to develop an efficient, cost-effective livestock sensing system. It collects and fuses accelerometer data and vision inputs to build a multimodal network for three tasks: image classification, object detection, and behavior recognition. The system is deployed and evaluated on commercial microcontrollers for real-time inference using embedded applications, demonstrating up to 270$\times$ model size reduction, less than 80ms response latency, and on-par performance comparable to existing methods. The incorporation of the wireless communication technique allows for seamless data transmission between devices, benefiting use cases in remote locations with poor Internet connectivity. This work delivers a robust, scalable IoT-edge livestock monitoring solution adaptable to diverse farming needs, offering flexibility for future extensions.
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