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Algorithms 2019, 12(1), 6; https://doi.org/10.3390/a12010006

Extraction and Detection of Surface Defects in Particleboards by Tracking Moving Targets

1
College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
2
College of Electrical Mechanical Engineering, Northeast Forestry University, Harbin 150040, China
*
Author to whom correspondence should be addressed.
Received: 30 November 2018 / Revised: 20 December 2018 / Accepted: 21 December 2018 / Published: 24 December 2018
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Abstract

Considering the linear motion of particleboards in the production line, the detection of surface defects in particleboards is a major challenge. In this paper, a method based on moving target tracking is proposed for the detection of surface defects in particleboards. To achieve this, the kernel correlation filter (KCF) target tracking algorithm was modified with the median flow algorithm and used to capture the moving targets of surface defects. The defect images were extracted by a Sobel operator, and the defect number, the defect area, and the degree of damage were calculated. The level of surface defect in particleboards was evaluated by fuzzy pattern recognition. Experiments were then carried out to prove the effectiveness and accuracy of the proposed method. View Full-Text
Keywords: particleboard defects detection; moving target tracking; kernel correlation filter; Sobel operator particleboard defects detection; moving target tracking; kernel correlation filter; Sobel operator
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Wang, C.; Liu, Y.; Wang, P. Extraction and Detection of Surface Defects in Particleboards by Tracking Moving Targets. Algorithms 2019, 12, 6.

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