智能选择要上传的图像，提升嵌入式设备目标检测准确率。
Optimizing Edge Offloading Decisions for Object Detection
- 根据本地检测结果估算上传收益，动态决定哪些图像该上传
- 在少量上传图像条件下，检测准确率显著优于现有方法
- 算法轻量，可在嵌入式设备上实时运行，适合资源受限场景
近年来，机器学习与硬件进步使得嵌入式设备能够以良好准确率实现实时目标检测。本文考虑一种场景：设备依赖本地检测器，但当本地精度不足时可将检测任务卸载至更强的边缘服务器。然而，资源限制导致可上传的图像数量有限。本文目标是在此约束下，通过识别应上传的图像来最大化整体检测准确率。为此，论文提出一种奖励指标，用于量化单个图像上传带来的潜在准确率提升，并设计了一种高效决策方法——仅基于本地检测结果估算该奖励。该方法计算开销低，可部署于嵌入式设备；实验表明，即使上传比例较低，其性能仍优于现有方案。
原文摘要 · Abstract (English)
Recent advances in machine learning and hardware have produced embedded devices capable of performing real-time object detection with commendable accuracy. We consider a scenario in which embedded devices rely on an onboard object detector, but have the option to offload detection to a more powerful edge server when local accuracy is deemed too low. Resource constraints, however, limit the number of images that can be offloaded to the edge. Our goal is to identify which images to offload to maximize overall detection accuracy under those constraints. To that end, the paper introduces a reward metric designed to quantify potential accuracy improvements from offloading individual images, and proposes an efficient approach to make offloading decisions by estimating this reward based only on local detection results. The approach is computationally frugal enough to run on embedded devices, and empirical findings indicate that it outperforms existing alternatives in improving detection accuracy even when the fraction of offloaded images is small.
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