Journal of Textile Research ›› 2019, Vol. 40 ›› Issue (03): 168-174.doi: 10.13475/j.fzxb.20180401607

• Management & Information • Previous Articles     Next Articles

Improvement recognition method of vamp's feature points based on machine vision

XU Yang(), ZHU Zhichao, SHENG Xiaowei, YU Zhiqi, SUN Yize   

  1. College of Mechanical Engineering, Donghua University, Shanghai 201620, China
  • Received:2018-04-10 Revised:2018-09-20 Online:2019-03-15 Published:2019-03-15

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Abstract:

Focusing on the problems of poor real-time,low efficiency and high cost of artificial recognition in vamps feature points,an improved method was proposed to automatically recognize the feature points of vamps by machine vision technology. Firstly,an improved median filter was used for preprocessing the grabbed images to eliminate noise interference. Secondly,by using the proposed adaptive threshold segmentation method, key regions of feature points were extracted. Finally,by morphological image processing and calculating the minimum circumscribed circle, the automatic identification of feature points was completed. In order to verify the reliability of the proposed method,group experiments were carried out on a large number of vamps samples under the condition of light intensity change and clutter,and the results were compared with the conventional one-dimensional and two-dimensional Otsu algorithm. The experimental results show that this method has better recognition accuracy and robustness in a variety of complex environments, the recognition success rate is above 93%, and the detection time is shorter than 0.5 s,which meets the demand of precision and real-time in industrial production.

Key words: vamp recognition, machine vision, improved median filter, adaptive threshold segmentation

CLC Number: 

  • TS101

Fig.1

Diagram of vamps detecting system"

Fig.2

Flow chart of feature points recognition algorithm"

Fig.3

Median filtering results. (a) Preprocessing-free results; (b) Processing results after median filtering"

Fig.4

Process diagram of improved fast filtering algorithm"

Fig.5

Median filtering result diagrams. (a) Conventional filtering; (b) Filtering algorithm proposed"

Tab.1

Comparison results of two preprocessing algorithms"

中值滤波
窗口大小/像素		 滤波方法 鞋面数量/
张		 平均滤波
时间/s
3 传统中值滤波 200 0.065
3 本文改进中值滤波 200 0.062
5 传统中值滤波 200 0.088
5 本文改进中值滤波 200 0.071

Tab.2

Contrast results of 3 algorithms under illumination change"

光照强度/
lx		 阈值
算法		 鞋面
数量/
张		 识别
成功/
张		 识别
成功
率/%		 平均
识别
时间/s
一维Otsu 200 35 17.5 0.18
30 二维Otsu 200 190 95.0 0.32
本文算法 200 188 94.0 0.17
一维Otsu 200 146 73.0 0.18
35 二维Otsu 200 192 96.0 0.35
本文算法 200 189 94.5 0.21
一维Otsu 200 158 79.0 0.20
40 二维Otsu 200 197 98.5 0.35
本文算法 200 195 97.5 0.19

Fig.6

Result diagrams of vamps threshold segmentation"

Fig.7

Segmentation curve diagrams of threshold algorithm proposed"

Fig.8

Result of segmentation diagrams under special conditions. (a) Inclination less than 30°; (b) Inversion"

Tab.3

Recognition results of algorithm in this paper under the special conditions"

混乱条件 鞋面
数量/张		 识别成功
数量/张		 识别
成功率/%		 平均识别
时间/s
倾斜30%以内 200 190 93 0.22
正反颠倒 200 185 93 0.21
添加0.05椒盐噪声 200 190 95 0.18

Fig.9

Results of diagram feature point processing. (a) Normal result; (b) Inclination result; (c) Inversion result"

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