Application and Performance Optimization of MapReduce Model in Image Segmentation

被引：1

作者：

Li, Maozhen ^{[1
,2
]}

Meng, Lu ^{[1
]}

Wang, Jiaying ^{[3
]}

Jin, Yong ^{[1
]}

Hu, Binyu ^{[1
]}

Chen, Youxing ^{[1
]}

机构：

[1] North Univ China, Sch Informat & Commun Engn, Taiyuan 030051, Peoples R China

[2] Brunel Univ London, Dept Elect & Comp Engn, Uxbridge UB8 3PH, Middx, England

[3] Univ New South Wales, Sch Elect Engn & Telecommun, Sydney, NSW 2052, Australia

来源：

IEEE ACCESS | 2020年 / 8卷

关键词：

Defects detection; data locality; image segmentation; MapReduce model; pipeline scheduling;

D O I：

10.1109/ACCESS.2019.2963343

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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 & x0025; 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.

引用

页码：31835 / 31844

页数：10

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