Journal of Textile Research ›› 2020, Vol. 41 ›› Issue (03): 160-167.doi: 10.13475/j.fzxb.20190601308

• Machinery & Accessories • Previous Articles     Next Articles

Method for position detection of cheese yarn rod

WANG Wensheng1(), LI Tianjian1, RAN Yuchen1, LU Ying2, HUANG Min1   

  1. 1. Mechanical Electrical Engineering School, Beijing Information Science & Technology University, Beijing 100192, China
    2. Beijing National Innovation Institute of Lightweight Ltd., Beijing 100083, China
  • Received:2019-06-06 Revised:2019-11-25 Online:2020-03-15 Published:2020-03-27

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Abstract:

In order to improve the detection accuracy of cheese yarn packages and to reduce wear to the package rods, an improved frequency-tuned salient (FT) significance detection algorithm is proposed to detect the cheese yarn rod position. Firstly, making use of the characteristics of the metal surface reflection of yarn rods head and the farther distance of the yarn rods from the bottom surface, the local illumination of the ring light source was applied to improve the target and background contrast. Then, using a priori knowledge of the target located near the center of the image, a block weighting template was designed to improve the FT algorithm and calculate the image saliency. The maximum between-class variance method (OTSU) automatic threshold segmentation was performed on the salient image to obtain the binary image. Then, the region which was obviously not the target is removed by morphological filtration. Finally, the coordinates of the final yarn rods head position were obtained by Hough transform circular fitting. The field experiment and comparison algorithm show that the improved method has the ability to resist defects. In addition, it has the ability to resist light changes and can be applied to the daytime and nighttime lighting scenes of the factory.

Key words: machine vision, cheese yarn package, position detection, saliency detection, threshold segmentation

CLC Number: 

  • TP311.1

Fig.1

Image area level division diagram"

Fig.2

Flow chart of yarn rods positioning detection algorithm"

Fig.3

Overall structure diagram of yarn rods positioning detection system"

Fig.4

Live picture of yarn rods positioning detection in factory"

Fig.5

Positioning program logic block diagram"

Fig.6

Detection program logic block diagram"

Fig.7

Different segmentation methods for normal standard images detection results. (a) Original; (b) OTSU algorithm;(c) Maximum entropy method; (d) SLIC algorithm; (e) FT algorithm; (f) Improved FT algorithm;(g) Improved FT algorithm morphology processing; (h) Improved FT final results"

Fig.8

Different segmentation methods for defective yarn rods images detection results. (a) Original; (b) OTSU algorithm; (c) Maximum entropy method; (d) SLIC algorithm; (e) FT algorithm; (f) Improved FT algorithm; (g) Improved FT algorithm morphology processing; (h) Improved FT final results"

Fig.9

Different segmentation methods for high exposure parameter images detection results. (a) Original; (b) OTSU algorithm; (c) Maximum entropy method; (d) SLIC algorithm; (e) FT algorithm; (f) Improved FT algorithm; (g) Improved FT algorithm morphology processing; (h) Improved FT final results"

Fig.10

Yarn rod positioning detection error analysis chart"

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