Sub-10cm diameter nano-drones are gaining momentum thanks to their
applicability in scenarios prevented to bigger flying drones, such as in narrow
environments and close to humans. However, their tiny form factor also brings
their major drawback: ultra-constrained memory and processors for the onboard
execution of their perception pipelines. Therefore, lightweight deep
learning-based approaches are becoming increasingly popular, stressing how
computational efficiency and energy-saving are paramount as they can make the
difference between a fully working closed-loop system and a failing one. In
this work, to maximize the exploitation of the ultra-limited resources aboard
nano-drones, we present a novel adaptive deep learning-based mechanism for the
efficient execution of a vision-based human pose estimation task. We leverage
two State-of-the-Art (SoA) convolutional neural networks (CNNs) with different
regression performance vs. computational costs trade-offs. By combining these
CNNs with three novel adaptation strategies based on the output's temporal
consistency and on auxiliary tasks to swap the CNN being executed proactively,
we present six different systems. On a real-world dataset and the actual
nano-drone hardware, our best-performing system, compared to executing only the
bigger and most accurate SoA model, shows 28% latency reduction while keeping
the same mean absolute error (MAE), 3% MAE reduction while being iso-latency,
and the absolute peak performance, i.e., 6% better than SoA model.

提出了一种新颖的自适应深度学习机制，用于有效执行基于视觉的人体姿势估计任务，以最大限度地利用纳米无人机上的极其有限资源，通过结合两种卷积神经网络、三种基于输出的时间一致性和辅助任务的新适应策略来实现。在真实世界数据集和实际纳米无人机硬件上，与仅执行更大且更精确的卷积神经网络模型相比，我们的最佳性能系统显示了 28% 的延迟减少，同时保持相同的平均绝对误差（MAE），3% 的 MAE 减少，并且绝对峰值性能，即比 SoA 模型好 6%。