Journal of Textile Research ›› 2021, Vol. 42 ›› Issue (12): 196-204.doi: 10.13475/j.fzxb.20210204309

• Comprehensive Review • Previous Articles    

Research progress in image segmentation and edge detection methods for alien fibers detection in cotton

REN Weijia1,2, DU Yuhong1,2(), ZUO Hengli1,2, YUAN Ruwang1,2   

  1. 1. School of Mechanical Engineering, Tiangong University, Tianjin 300387, China
    2. Tianjin Key Laboratory of Advanced Mechatronics Equipment Technology, Tianjin 300387, China
  • Received:2021-02-15 Revised:2021-09-06 Online:2021-12-15 Published:2021-12-29
  • Contact: DU Yuhong E-mail:dyh202@163.com

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Abstract:

In order to further improve the detection efficiency for picking up alien fibers among cotton, image processing methods for detecting alien fibers were reviewed. This paper analyzed the inaccurate location, background blur and the influence of noise in edge detection methods, and studied the edge continuity and segmentation effect of different alien fibers in the image segmentation methods. The common edge detection methods and image segmentation methods for alien fibers among cotton were discussed, advantages and limitations of various processing methods were analyzed, and the detection methods applicable to various alien fibers were summarized, pointing out the existing problems and deficiencies in current practice. It is concluded that different image processing methods are currently applied to detect different types of alien fibers, and it is not possible to detect all types of alien fibers at the same time. The paper highlighted that the suitable detection algorithms should be selected and combined according to the specific types, contents, physical characteristics of alien fibers to develop an universal algorithm in order to reduce the cost and calculation burden.

Key words: alien fiber, image pre-processing, edge detection, image segmentation, online inspection

CLC Number: 

  • TS111.9

Fig.1

Diagram of foreign fiber detection method"

Fig.2

Kirsch operator convolution template direction"

Tab.1

Advantages and disadvantages of edge detection algorithms"

算子 优缺点比较
Roberts 对目标与背景差异性大且噪声较少的图像处理效果较好,但Roberts算子边缘定位效果不理想,提取边缘较粗
Prewitt 对渐变灰度值和噪声较多的异性纤维图像处理效果较好
Sobel 对渐变灰度值和噪声较多的图像处理效果较好,平滑效果较Prewitt算子好,Sobel算子边缘定位效果不理想
Kirsch 对渐变灰度值和噪声较多的异性纤维图像处理效果较好
Canny 该方法一直是经典边缘检测算法,对噪声抑制效果较好,定位精度较高,能够检测到真正的弱边缘,对细小异性纤维的检测效果较好
Laplacian 对图像中阶跃性边缘点定位准确,对噪声敏感,易丢失部分边缘的方向信息,边缘检测不连续
LOG LOG算子预先对图像进行高斯平滑处理,在一定程度上克服了噪声的影响,多用来判断边缘像素位于明区还是暗区

Fig.3

Image segmentation effect comparison. (a) Original image; (b) Grayscale image; (c) Otsu method image; (d) Two-dimensional Otsu method"

Fig.4

Reference [39] method detection effect. (a) Original image; (b) Approximate difference molecular map; (c) Vertical difference molecular map; (d) Level difference molecular map; (e) Approximate difference molecular map segmentation; (f) Vertical difference molecular map segmentation; (g) Level difference molecular map segmentation; (h) Fusion image"

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