# Yarn Angle Detection of Glass Fiber Plain Weave Fabric Based on Machine Vision
## 1 Introduction  
Glass fiber plain weave fabric is formed by interweaving warp and weft yarns; the internal spacing of warp and weft yarns is more uniform, with high stability, and it is commonly used in numerous industries to make composite materials. The yarn angle (the angle between the warp and weft yarns) is an important parameter affecting the mechanical properties of woven composites [1; 2; 3; 4; 5]. It is also possible to determine whether there are folds and foreign objects in the glass fiber plain weave fabric by analyzing the angles. The research of [PERSON] showed that a range of variations in yarn angle can affect the longitudinal tensile modulus and strength of the fabric [6]. During the stretching process of the fabric, the warp and weft yarns may deviate or overlap, and the angle between the yarns may change to some extent, thus affecting the performance of the composite material [7]. The change in yarn angle, that is, the angle between the warp and weft in fiber fabrics, affects the relative relationship between the warp and weft. As fiber fabrics are applied to composite material molding, they ultimately affect the distribution of fibers in the composite material, thereby affecting the mechanical properties of the composite material.  
Traditional yarn angle detection is usually conducted by manual measurement, which is less efficient, and on the surface of the glass fiber plain weave fabric exists specular reflection, so long-time observation of the fabric will also cause some impact on the eyes.  
X-ray computed tomography (CT) scans, intensity methods, and current methods are also used to measure fiber orientation. However, these methods also inevitably have a certain impact on the human body, due to radiation, and contact measurements can cause some damage to the material. The research of [PERSON] showed that the angle according to the nature of the two-dimensional Fourier transform [8] is suitable for one-way fabric angle detection, with high accuracy and good stability, but is not suitable for measuring the yarn angle. [PERSON] obtained the minimum circumscribed rectangle of the target region by edge detection and the rotating caliper algorithm, which was calculated to obtain the fiber orientation [9]. The Hough transform is widely used to obtain the orientation and angle information of the target object after processing the image [10; 11; 12; 13].  
Some also use target detection to detect a specific part and obtain the angle based on the change in this part [14]. [PERSON] improved the accuracy of pavement texture depth measurement by investigating the camera pole angle using the photometric stereo vision method [15]. When the angle between the light source and the camera changes, the acquired image will also change. [PERSON] used image fusion to restore the target area and obtain the yarn angle of the carbon fiber fabric through edge detection [16].  
Currently, scholars' research mainly focuses on the angle detection of carbon fibers, and there has been no research on the angle detection of glass fibers. The existence of specular reflection on glass fiber plain weave fabric causes some interference to detection, and a method is proposed to detect the yarn angle of glass fiber plain weave fabric, which can effectively reduce the effect of specular reflection. Line segment detection is performed on the initial image by Otsu threshold segmentation [17], skeleton extraction [18], and Hough transform, and the confidence degree of the line segment is calculated. When the confidence degree meets the requirement, the yarn angle is obtained by calculating the angle between the line segments. When the confidence degree does not meet the requirement, the contour of the region formed by the extrusion of the warp and weft yarns is reconstructed by calculating the center of mass and the morphological operations, and analyzing the two adjacent edges of the contour to obtain the yarn angle.