一种基于深度可分卷积的分离边缘计算可行目标检测网络

Qingfeng Wen, Wei Guo, Longji Li, Boyu Fan, Zaifeng Shi
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引用次数: 0

摘要

由于其出色的分布式数据处理效率,边缘计算已成为目标检测领域的研究热点。卷积神经网络(CNN)极大地提高了机器视觉的识别性能,但由于其庞大的数据量和计算量,难以在边缘计算设备上部署。传统的部署框架完全在云中心或边缘设备上运行CNN,而cloud-only方法导致无法忍受的延迟和带宽消耗,edge-only方法在支持大量计算任务时导致边缘设备故障。本文提出了一种高效的边缘计算框架,并构建了一个小型目标检测网络:分裂边缘计算可行网络(SECDN)。在该框架中,特征提取部分在边缘设备上实现,并对参数进行了压缩,避免了边缘设备的高计算成本。原始数据在本地进行预处理,结果发送到云中心进行最终处理。SECDN实现了边缘和云的协同工作,减轻了边缘或云的压力。实验结果表明,与现有网络相比,SECDN的检测精度没有明显下降,所需的数据量和计算量也大大减少。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Split Edge Computing Doable Network for Object Detection base on Depthwise Separable Convolution
Due to the outstanding distributed data processing efficiency, edge computing has become a research hotspot in the field of object detection. Convolutional Neural Network (CNN) improves recognition performance of machine vision greatly, yet, which is difficult to deploy on edge computing device for its huge amount of data and calculation. Traditional deployment frameworks operate CNN completely on the cloud center or edge devices, while the cloud-only method leads to intolerable delay and bandwidth consumption, the edge-only causes the failure of edge devices when supporting massive computing tasks. In this paper, we propose an efficient edge computing framework and build a small target detection network: Split Edge Computing Doable Network (SECDN). In this framework, the feature extraction part is implemented on the edge device, and the parameters are compressed to avoid high calculation cost of edge devices. Raw data is preprocessed locally, and the results are sent to the cloud center for final processing. SECDN realizes the collaborative work of edge and cloud, and reduces pressure of edge or cloud. The experimental results show that the detection accuracy of SECDN has no obvious worse compared with the state of art network while requiring much lower data and computing effort.
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