An Improved Elbows Detection Algorithm for Underwater Blurred Images

DOI

10.2991/eame-17.2017.62How to use a DOI?

Keywords

pipeline detection; image processing; hough transform; k-means clustering

Abstract

In order to reduce the influence of noise pollution, shadow, biofouling and disruptors on elbows detection failure rate, an underwater pipeline detection algorithm was proposed. To improve the detection accuracy of underwater pipeline, two aspects of research were presented. One is the improvement of edge detection method; a filtering method based on region saturation control was designed to filter out the edge feature of underwater interference. Another is a method of pipeline detection based on linear feature clustering, including the Hough transform and K-means clustering algorithm, which is used to deal with the binary image of pipeline when the pipeline detection failure appears. As a result, the detection accuracy of underwater pipeline detection proposed method is higher than that of traditional pipeline detection algorithm.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Yihui Liu
AU  - Lei Wan
AU  - Mingwei Sheng
AU  - Tao Liu
AU  - Yueming Li
PY  - 2017/04
DA  - 2017/04
TI  - An Improved Elbows Detection Algorithm for Underwater Blurred Images
BT  - Proceedings of the 2017 2nd International Conference on Electrical, Automation and Mechanical Engineering (EAME 2017)
PB  - Atlantis Press
SP  - 264
EP  - 267
SN  - 2352-5401
UR  - https://doi.org/10.2991/eame-17.2017.62
DO  - 10.2991/eame-17.2017.62
ID  - Liu2017/04
ER  -