Abstract
With the rapid development of AI, machine vision has been applied to industrial defect detection widely. These methods have high requirements for computing and networking resources to ensure the detection task has high accuracy and low latency. Therefore, it has become an important research direction that reducing network and computer pressure when improving the detection quality in industrial field. This paper proposes a defect detection method and system based on edge computing to improve defect detection capability and efficiency. Firstly, the resource perception module, task scheduling module and corresponding method are proposed to realize the linkage between edge resources and production line tasks. Secondly, a joint execution strategy is proposed according to the characteristics of defect detection, the intelligent gateway and Mobile edge computing resources are reasonably allocated to perform tasks, so as to realize the optimal utilization of edge resources. Finally, the system is applied to the defect detection in the surface mounted technology production line, which can improve the detection efficiency.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhao, L., Wu, Y.: Research progress of surface defect detection methods based on machine vision. Chinese J. Sci. Instrum. (2022)
Bing, H., Wang, J.: Detection of PCB surface defects with improved faster-RCNN and feature pyramid network. IEEE Access 8, 108335–108345 (2020). https://doi.org/10.1109/ACCESS.2020.3001349
Su, H., et al.: Review of research on the inspection of surface defect based on visual perception. Comput. Integr. Manuf. Syst. 27(4), 1–31 (2021)
Qiu, T., et al.: Edge computing in industrial internet of things: architecture, advances and challenges. IEEE Commun. Surv. Tutor. 22(4), 2462–2488 (2020)
Miao, Y., et al.: Intelligent task prediction and computation offloading based on mobile-edge cloud computing. Futur. Gener. Comput. Syst. 102, 925–931 (2020)
Tang, B., Kong, J., Wu, S.: Review of surface defect detection based on machine vision. J. Image Graph. 22(12), 1640–1663 (2017)
Cao, W., Liu, Q., He, Z.: Review of pavement defect detection methods. IEEE Access 8, 14531–14544 (2020)
Chen, B., et al.: Edge computing in IoT-based manufacturing. IEEE Commun. Mag. 56(9), 103–109 (2018)
Udhayakumar, P., Kumanan, S.: Sequencing and scheduling of job and tool in a flexible manufacturing system using ant colony optimization algorithm. Int. J. Adv. Manuf. Technol. 50(9–12), 1075–1084 (2010)
Tao, X., Hou, W., Xu, D.: A Survey of surface defect detection methods based on deep learning. ACTA Autom. Sci. 47(5), 1017–1034 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Xue, Y., Shen, Y., Duan, H. (2023). Industrial Defect Detection System Based on Edge-Cloud Collaboration and Task Scheduling Technology. In: Wang, Y., Liu, Y., Zou, J., Huo, M. (eds) Signal and Information Processing, Networking and Computers. ICSINC 2022. Lecture Notes in Electrical Engineering, vol 996. Springer, Singapore. https://doi.org/10.1007/978-981-19-9968-0_13
Download citation
DOI: https://doi.org/10.1007/978-981-19-9968-0_13
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-9967-3
Online ISBN: 978-981-19-9968-0
eBook Packages: Computer ScienceComputer Science (R0)