A cloud-edge collaborative scheme for surface defect recognition based on KubeEdge

Bobo Zong; Guoming Cai; Kaijun Huang; Miao Wang

doi:10.1117/12.2659311

28 October 2022 A cloud-edge collaborative scheme for surface defect recognition based on KubeEdge

Bobo Zong, Guoming Cai, Kaijun Huang, Miao Wang

Proceedings Volume 12453, Third International Conference on Computer Communication and Network Security (CCNS 2022); 124531E (2022) https://doi.org/10.1117/12.2659311
Event: Third International Conference on Computer Communication and Network Security (CCNS 2022), 2022, Hohhot, China

Abstract

With the expansion of smart devices in 5G, Internet of Things, mobile Internet and other technical scenarios, the number of devices on the edge of the network has increased dramatically. In the edge computing scene, there are a large number of heterogeneous devices, each of which has its own unique characteristics and attributes. For the edge scene, due to the increasing requirements of massive data on the timeliness, security and network dependence of computing facilities, the current cloud platform with Kubernetes as the core is not fully applicable. Therefore, many open source frameworks came into being, and KubeEdge [1] is one of the representatives. Aiming at KubeEdge, this paper proposes a cloud-edge collaboration scheme, which deploys the surface defect recognition algorithm based on YOLOv5 network to cloud edge devices to realize surface defect recognition and node autonomy in edge scenes, and provides a solution for cloud edge collaboration scenes.

Citation Download Citation

Bobo Zong, Guoming Cai, Kaijun Huang, and Miao Wang "A cloud-edge collaborative scheme for surface defect recognition based on KubeEdge", Proc. SPIE 12453, Third International Conference on Computer Communication and Network Security (CCNS 2022), 124531E (28 October 2022); https://doi.org/10.1117/12.2659311

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KEYWORDS

Clouds

Data modeling

Evolutionary algorithms

Artificial intelligence

Data communications

Detection and tracking algorithms

Instrument modeling

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