Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/31877
Title: Application and Performance Optimization of MapReduce Model in Image Segmentation
Authors: Li, M
Meng, L
Wang, J
Jin, Y
Hu, B
Chen, Y
Keywords: defects detection;data locality;image segmentation;MapReduce model;pipeline scheduling
Issue Date: 31-Dec-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: Li, M. et al. (2020) 'Application and Performance Optimization of MapReduce Model in Image Segmentation', IEEE Access, 8, pp. 31835 - 31844. doi: 10.1109/ACCESS.2019.2963343.
Abstract: With the increase of glass detection speed, some defects of MapReduce distributed computing framework are exposed, and the processing speed and timeliness cannot meet the requirements of glass-defect detection in industrial technology. Based on the MapReduce distributed computing framework, this paper designs a threshold segmentation method to complete the segmentation of glass-defect images. By improving the replication placement strategy and pipeline scheduling mechanism, the computing and storage are localized, and the timeliness of data processing is accelerated. The experimental results show that the improved MapReduce computing framework has an average increase of 14.8% in processing speed. It can detect the glass ribbon running at 800m/h and also detect the number, position and type of defects on the glass ribbon.
URI: https://bura.brunel.ac.uk/handle/2438/31877
DOI: https://doi.org/10.1109/ACCESS.2019.2963343
Other Identifiers: ORCiD: Maozhen Li https://orcid.org/0000-0002-0820-5487
ORCiD: Lu Meng https://orcid.org/0000-0002-5990-4106
ORCiD: Jiaying Wang https://orcid.org/0000-0003-3992-0771
ORCiD: Yong Jin https://orcid.org/0000-0002-7664-1416
ORCiD: Binyu Hu https://orcid.org/0000-0003-4668-9459
ORCiD: Youxing Chen https://orcid.org/0000-0002-8915-2689
Appears in Collections:Dept of Electronic and Electrical Engineering Research Papers

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