Automatic detection and counting of circular and rectangular steel bars

Muhammad Faiz Ghazali; Lai Kuan Wong; John See

doi:10.1007/978-981-10-1721-6_22

Automatic detection and counting of circular and rectangular steel bars

Muhammad Faiz Ghazali^*, Lai Kuan Wong, John See

^*Corresponding author for this work

School of Mathematical & Computer Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 Citations (Scopus)

Abstract

The steel industry heavily relies on manual labor and the use of photoelectric sensors and complex counting machines to count steel bars. In the last decade, research on the automatic detection and counting of steel bars by using image processing and computer vision techniques have seen much progress. Nevertheless, most of past research focused mainly on circular shaped steel bars from a direct frontal camera angle. In this paper, we propose a method that is adaptable to both circular and rectangular shaped steel bars, and robust towards different camera angles and lighting intensity. The captured digital image first undergoes an essential pre-processing stage followed by edge detection which extracts the steel bar edges. For circular shaped steel bars, we apply Hough Transform followed by a post-process while the rectangular ones can be accurately found based on a series of morphological operations. Experiments conducted on a variety of challenging conditions demonstrate the capability of our approach to a good measure of success.

Original language	English
Title of host publication	9th International Conference on Robotic, Vision, Signal Processing and Power Applications
Editors	Haidi Ibrahim, Shahid Iqbal, Soo Siang Teoh, Mohd Mustaffa
Publisher	Springer
Pages	199-207
Number of pages	9
ISBN (Electronic)	9789811017216
ISBN (Print)	9789811017193
DOIs	https://doi.org/10.1007/978-981-10-1721-6_22
Publication status	Published - 30 Sept 2016
Event	9th International Conference on Robotic, Vision, Signal Processing and Power Applications 2016 - , Malaysia Duration: 2 Feb 2016 → 3 Feb 2016

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	398
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	9th International Conference on Robotic, Vision, Signal Processing and Power Applications 2016
Abbreviated title	RoViSP 2016
Country/Territory	Malaysia
Period	2/02/16 → 3/02/16

Keywords

Circular steel bar
Rectangular steel bar
Steel bar counting
Steel bar detection

ASJC Scopus subject areas

Industrial and Manufacturing Engineering

Access to Document

10.1007/978-981-10-1721-6_22

Cite this

Ghazali, M. F., Wong, L. K., & See, J. (2016). Automatic detection and counting of circular and rectangular steel bars. In H. Ibrahim, S. Iqbal, S. S. Teoh, & M. Mustaffa (Eds.), 9th International Conference on Robotic, Vision, Signal Processing and Power Applications (pp. 199-207). (Lecture Notes in Electrical Engineering; Vol. 398). Springer. https://doi.org/10.1007/978-981-10-1721-6_22

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title = "Automatic detection and counting of circular and rectangular steel bars",

abstract = "The steel industry heavily relies on manual labor and the use of photoelectric sensors and complex counting machines to count steel bars. In the last decade, research on the automatic detection and counting of steel bars by using image processing and computer vision techniques have seen much progress. Nevertheless, most of past research focused mainly on circular shaped steel bars from a direct frontal camera angle. In this paper, we propose a method that is adaptable to both circular and rectangular shaped steel bars, and robust towards different camera angles and lighting intensity. The captured digital image first undergoes an essential pre-processing stage followed by edge detection which extracts the steel bar edges. For circular shaped steel bars, we apply Hough Transform followed by a post-process while the rectangular ones can be accurately found based on a series of morphological operations. Experiments conducted on a variety of challenging conditions demonstrate the capability of our approach to a good measure of success.",

keywords = "Circular steel bar, Rectangular steel bar, Steel bar counting, Steel bar detection",

author = "Ghazali, {Muhammad Faiz} and Wong, {Lai Kuan} and John See",

note = "Publisher Copyright: {\textcopyright} Springer Science+Business Media Singapore 2017. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.; 9th International Conference on Robotic, Vision, Signal Processing and Power Applications 2016, RoViSP 2016 ; Conference date: 02-02-2016 Through 03-02-2016",

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Ghazali, MF, Wong, LK & See, J 2016, Automatic detection and counting of circular and rectangular steel bars. in H Ibrahim, S Iqbal, SS Teoh & M Mustaffa (eds), 9th International Conference on Robotic, Vision, Signal Processing and Power Applications. Lecture Notes in Electrical Engineering, vol. 398, Springer, pp. 199-207, 9th International Conference on Robotic, Vision, Signal Processing and Power Applications 2016, Malaysia, 2/02/16. https://doi.org/10.1007/978-981-10-1721-6_22

Automatic detection and counting of circular and rectangular steel bars. / Ghazali, Muhammad Faiz; Wong, Lai Kuan; See, John.
9th International Conference on Robotic, Vision, Signal Processing and Power Applications. ed. / Haidi Ibrahim; Shahid Iqbal; Soo Siang Teoh; Mohd Mustaffa. Springer, 2016. p. 199-207 (Lecture Notes in Electrical Engineering; Vol. 398).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Automatic detection and counting of circular and rectangular steel bars

AU - Ghazali, Muhammad Faiz

AU - Wong, Lai Kuan

AU - See, John

PY - 2016/9/30

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N2 - The steel industry heavily relies on manual labor and the use of photoelectric sensors and complex counting machines to count steel bars. In the last decade, research on the automatic detection and counting of steel bars by using image processing and computer vision techniques have seen much progress. Nevertheless, most of past research focused mainly on circular shaped steel bars from a direct frontal camera angle. In this paper, we propose a method that is adaptable to both circular and rectangular shaped steel bars, and robust towards different camera angles and lighting intensity. The captured digital image first undergoes an essential pre-processing stage followed by edge detection which extracts the steel bar edges. For circular shaped steel bars, we apply Hough Transform followed by a post-process while the rectangular ones can be accurately found based on a series of morphological operations. Experiments conducted on a variety of challenging conditions demonstrate the capability of our approach to a good measure of success.

AB - The steel industry heavily relies on manual labor and the use of photoelectric sensors and complex counting machines to count steel bars. In the last decade, research on the automatic detection and counting of steel bars by using image processing and computer vision techniques have seen much progress. Nevertheless, most of past research focused mainly on circular shaped steel bars from a direct frontal camera angle. In this paper, we propose a method that is adaptable to both circular and rectangular shaped steel bars, and robust towards different camera angles and lighting intensity. The captured digital image first undergoes an essential pre-processing stage followed by edge detection which extracts the steel bar edges. For circular shaped steel bars, we apply Hough Transform followed by a post-process while the rectangular ones can be accurately found based on a series of morphological operations. Experiments conducted on a variety of challenging conditions demonstrate the capability of our approach to a good measure of success.

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Y2 - 2 February 2016 through 3 February 2016

ER -

Automatic detection and counting of circular and rectangular steel bars

Abstract

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Conference

Keywords

ASJC Scopus subject areas

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