Edge devices, such as cameras and mobile units, are increasingly capable of performing sophisticated computation in addition to their traditional roles in sensing and communicating signals. The focus of this paper is on collaborative object detection, where deep features computed on the edge device from input images are transmitted to the cloud for further processing. We consider the impact of packet loss on the transmitted features and examine several ways for recovering the missing data. In particular, through theory and experiments, we show that methods for image inpainting based on partial differential equations work well for the recovery of missing features in the latent space. The obtained results represent the new state of the art for missing data recovery in collaborative object detection.

翻译：除了在感测和通信信号方面的传统作用外,像照相机和移动装置等边缘装置越来越有能力进行精密的计算,本文的重点是合作物体探测,从输入图像中计算出的边缘装置的深层特征被传送到云层,以便进一步处理。我们考虑了包装损失对传输特征的影响,并研究了恢复缺失数据的若干方法。我们特别通过理论和实验,表明基于部分差异方程式的图像涂色方法对恢复潜藏空间缺失特征非常有效。获得的结果代表了合作物体探测中缺少数据的最新状态。