The utilization of AIoT technology has become a crucial trend in modern poultry management, offering the potential to optimize farming operations and reduce human workloads. This paper presents a real-time and compact edge-AI enabled detector designed to identify chickens and their healthy statuses using frames captured by a lightweight and intelligent camera equipped with an edge-AI enabled CMOS sensor. To ensure efficient deployment of the proposed compact detector within the memory-constrained edge-AI enabled CMOS sensor, we employ a FCOS-Lite detector leveraging MobileNet as the backbone. To mitigate the issue of reduced accuracy in compact edge-AI detectors without incurring additional inference costs, we propose a gradient weighting loss function as classification loss and introduce CIOU loss function as localization loss. Additionally, we propose a knowledge distillation scheme to transfer valuable information from a large teacher detector to the proposed FCOS-Lite detector, thereby enhancing its performance while preserving a compact model size. Experimental results demonstrate the proposed edge-AI enabled detector achieves commendable performance metrics, including a mean average precision (mAP) of 95.1$\%$ and an F1-score of 94.2$\%$, etc. Notably, the proposed detector can be efficiently deployed and operates at a speed exceeding 20 FPS on the edge-AI enabled CMOS sensor, achieved through int8 quantization. That meets practical demands for automated poultry health monitoring using lightweight intelligent cameras with low power consumption and minimal bandwidth costs.

翻译：AIoT技术的应用已成为现代家禽管理的关键趋势，其具备优化养殖操作与降低人工工作负荷的潜力。本文提出了一种实时且紧凑的边缘AI检测器，旨在利用配备边缘AI CMOS传感器的轻量智能相机所捕获的帧图像，识别鸡只及其健康状态。为确保所提出的紧凑型检测器能在内存受限的边缘AI CMOS传感器中高效部署，我们采用了以MobileNet为骨干网络的FCOS-Lite检测器。为缓解紧凑型边缘AI检测器精度下降的问题且不增加额外推理成本，我们提出了一种梯度加权损失函数作为分类损失，并引入CIOU损失函数作为定位损失。此外，我们提出了一种知识蒸馏方案，将大型教师检测器中的有价值信息迁移至所提出的FCOS-Lite检测器中，从而在保持紧凑模型尺寸的同时提升其性能。实验结果表明，所提出的边缘AI检测器取得了优异的性能指标，包括95.1$\%$的平均精度均值（mAP）和94.2$\%$的F1分数等。值得注意的是，通过int8量化，所提出的检测器可高效部署并在边缘AI CMOS传感器上以超过20 FPS的速度运行。这满足了使用低功耗、低带宽成本的轻量智能相机进行自动化家禽健康监测的实际需求。