Edge Computing Based Intelligent Perception for Industrial Video Network: Challenge and Progress
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摘要: 工业视频网络是由工业网络系统现场层的视觉感知终端组成的网络, 是实现工业网络系统泛在感知的重要基石. 通过支持边缘计算层和现场设备层之间的交互和物联, 工业视频网络将独立的视觉传感器单元无线连接、边缘处理, 以实现空间分散下的协作监控和精确感知. 它具有感知维度高, 网络动态性强, 感知与传输、计算、存储紧密耦合等突出特性. 如何在计算、网络、存储资源受限环境下实现终端压缩提纯、边缘协作处理、云端敏捷分析, 是这类系统研究的新挑战. 本文首先简述工业视频网络的定义和主要特征; 其次分析工业视频网络智能感知面临的挑战和关键问题; 然后综述基于边缘计算的工业视频网络智能感知关键技术的研究进展; 最后对工业视频网络智能感知的未来研究方向和潜在应用前景进行总结和展望.Abstract: Industrial video network is a network composed of visual perception terminals at the field layer of industrial network systems, which is an important cornerstone for achieving ubiquitous perception in industrial network systems. By supporting the interaction and connection between the edge computing layer and the field device layer, the industrial video network wirelessly connects and processes the edges of independent visual sensor units to achieve collaborative monitoring and accurate perception under spatial dispersion. It has outstanding characteristics such as high perception dimension, strong network dynamics, close coupling between perception and transmission, computing and storage. How to achieve terminal compression and purification, edge collaborative processing, and cloud agile analysis under limited computing, network and storage resources is a new challenge in the research of such systems. This paper firstly describes the definition and main characteristics of industrial video network; Secondly, the challenges and key issues of industrial video network intelligent perception are analyzed; Thirdly, the research progress of intelligent perception key technologies for edge computing based industrial video network are summarized; Finally, the future research direction and potential application prospect of industrial video network intelligent perception are summarized and prospected.
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图 5 基于OMAF标准的全景视频主要处理流程
Fig. 5 Main procedure of 360-degree videos based on OMAF standard
图 8 卷积层和全连接层的并行运行
Fig. 8 Parallel running of convolutional layers and fully-connected layers
图 9 不同视频分辨率对边缘感知算法精度和 响应时延的影响
Fig. 9 Effect of different video resolutions on accuracy and response latency of edge perception algorithm
图 11 不同时延限制对精度的影响
Fig. 11 The impact of different latency restrictions on the accuracy
图 13 工业视频网络在飞机总装产线现场监控中的应用
Fig. 13 Application of industrial video network to field monitoring of aircraft assembly line
表 1 映射方法对比
Table 1 Comparison of projection methods
映射方法 像素比例 优点 缺点 经纬图映射 1.57 映射方法简单 南北两级存在畸变 立方体映射 1.91 每个面失真小 多面拼接不连续 八面体映射 1.10 映射面积小 多面拼接不连续 二十面体映射 1.21 映射面积小 多面拼接不连续 柱状映射 1.09 映射面积小 存在空白区域 
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表 2 在VehicleID上不同图片污染程度下的DFR-ST算法性能对比
Table 2 Performance comparison of DFR-ST algorithm with different image contamination degrees on VehicleID
污染程度 测试集数量 = 800 测试集数量 = 1600 测试集数量 = 2400 mAP Top-1 Top-5 mAP Top-1 Top-5 mAP Top-1 Top-5 0% 87.76 82.15 95.39 84.94 79.33 92.76 83.18 77.93 89.52 5% 84.47 78.83 91.41 81.50 75.99 88.32 80.09 74.77 85.48 10% 82.55 76.92 89.71 79.36 73.72 86.88 78.72 73.49 85.04 20% 69.18 61.13 78.73 68.35 61.37 76.98 64.38 56.63 73.70 30% 65.75 58.62 73.69 61.53 53.59 71.10 58.24 50.46 67.29 40% 61.61 54.12 70.35 59.77 51.84 69.21 54.81 46.84 63.73 50% 60.66 52.47 69.93 56.06 47.75 65.64 53.70 45.35 63.24
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