Decentralized edge computing techniques have been attracted strongly attentions in many applications of intelligent Internet of Things (IIoT). Among these applications, intelligent edge surveillance (INES) methods play a very important role to recognize object feature information automatically from surveillance video by virtue of edge computing together with image processing and computer vision. Traditional centralized surveillance techniques recognize objects at the cost of high latency, high cost and also require high occupied storage. In this paper, we propose a deep learning-based INES technique for a specific IIoT application. First, a depthwise separable convolutional strategy is introduced to build a lightweight deep neural network to reduce its computational cost. Second, we combine edge computing with cloud computing to reduce network traffic. Third, our proposed INES method is applied into the practical construction site for the validation of a specific IIoT application. The detection speed of the proposed INES reaches 16 frames per second in the edge device. After the joint computing of edge and cloud, the detection precision can reach as high as 89%. In addition, the operating cost at the edge device is only one-tenth of that of the centralized server. Experiment results are given to confirm the proposed INES method in terms of both computational cost and detection accuracy.

中文翻译：

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基于轻量级深度学习的智能边缘监控技术

去中心化边缘计算技术在智能物联网 (IIoT) 的许多应用中引起了强烈关注。在这些应用中，智能边缘监控（INES）方法在利用边缘计算结合图像处理和计算机视觉从监控视频中自动识别目标特征信息方面发挥着非常重要的作用。传统的集中监控技术以高延迟、高成本和高占用存储为代价来识别对象。在本文中，我们为特定的 IIoT 应用提出了一种基于深度学习的 INES 技术。首先，引入深度可分离卷积策略来构建轻量级深度神经网络以降低其计算成本。第二，我们将边缘计算与云计算相结合，以减少网络流量。第三，我们提出的 INES 方法应用于实际施工现场，以验证特定的 IIoT 应用。所提出的INES的检测速度在边缘设备中达到每秒16帧。经过边缘和云的联合计算，检测精度可达89%。此外，边缘设备的运营成本仅为中心服务器的十分之一。给出了实验结果以在计算成本和检测精度方面证实所提出的INES方法。经过边缘和云的联合计算，检测精度可达89%。此外，边缘设备的运营成本仅为中心服务器的十分之一。给出了实验结果以在计算成本和检测精度方面证实所提出的INES方法。经过边缘和云的联合计算，检测精度可达89%。此外，边缘设备的运营成本仅为中心服务器的十分之一。给出了实验结果以在计算成本和检测精度方面证实所提出的INES方法。