BlankSkip： Nano无人机上的早期退出目标检测
BlankSkip: Early-exit Object Detection onboard Nano-drones
AI总结：
本文提出BlankSkip，一种用于无人机目标检测的自适应网络，通过简单辅助分类任务实现早期退出，提升推理效率，实验显示在Nano无人机平台上实现24%的吞吐量提升，仅牺牲0.015mAP。
摘要：
在微型无人机上部署小型计算机视觉深度神经网络（DNN）是实现自主性的关键，但受限于其极小的计算平台（约10MiB内存，1W功率预算）。早期退出自适应DNN通过降低“易处理”输入帧的计算量可减少平均推理延迟。然而，尽管此方法在分类任务中被广泛研究，其在密集任务如目标检测（OD）中的应用并不直接。本文提出BlankSkip，一种用于设备端OD的自适应网络，利用简单辅助分类任务实现早期退出，即识别无目标的帧。通过使用真实世界微型无人机平台Bitcraze Crazyflie 2.1进行实验，在最先进的微型无人机OD数据集上，与静态MobileNet-SSD检测器相比，实现了24%的平均吞吐量提升，仅牺牲0.015的平均精度（mAP）。
英文摘要：
Deploying tiny computer vision Deep Neural Networks (DNNs) on-board nano-sized drones is key for achieving autonomy, but is complicated by the extremely tight constraints of their computational platforms (approximately 10 MiB memory, 1 W power budget). Early-exit adaptive DNNs that dial down the computational effort for "easy-to-process" input frames represent a promising way to reduce the average inference latency. However, while this approach is extensively studied for classification, its application to dense tasks like object detection (OD) is not straightforward. In this paper, we propose BlankSkip, an adaptive network for on-device OD that leverages a simple auxiliary classification task for early exit, i.e., identifying frames with no objects of interest. With experiments using a real-world nano-drone platform, the Bitcraze Crazyflie 2.1, we achieve up to 24% average throughput improvement with a limited 0.015 mean Average Precision (mAP) drop compared to a static MobileNet-SSD detector, on a state-of-the-art nano-drones OD dataset.
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