Adaptive Deep Learning for Efficient Visual Pose Estimation aboard Ultra-low-power Nano-drones

TL;DR

This paper introduces an adaptive deep learning system for visual human pose estimation on ultra-low-power nano-drones, balancing accuracy and efficiency through novel strategies and multiple CNN models.

Contribution

It proposes a new adaptive mechanism combining multiple CNNs and strategies to optimize pose estimation efficiency on resource-constrained nano-drones.

Findings

28% latency reduction with same accuracy

3% MAE reduction at equal latency

6% peak performance improvement

Abstract

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.