## 1 Introduction
For images of any size, the distance matching function has a faster calculation speed than the traditional co-occurrence matrix method. It is suitable for patterns of finite size. However, when the brightness and shape of the periodic pattern are inconsistent, the distance matching function cannot effectively extract the pattern period. The method based on an autocorrelation function calculates the correlation coefficient of the texture by the autocorrelation function of the image to analyze the periodicity of the texture. [PERSON] [9] calculated the autocorrelation function of the texture edge to determine the matrix of the autocorrelation function, and  
Figure 1: Textile cutting piece of car seat and pattern parameters. (**a**) The overall outline of the cutting piece with strip-shaped pattern in the global perspective. (**b**) The local part of (a) where \(T_{h}\) is weft period length, \(T_{v}\) is warp period length. \(\theta_{uvfl}\) is weft skew angle, and \(\theta_{unap}\) is warp skew angle. then extracted the periodic and directional features of the texture. The image autocorrelation function method is easy to implement and has strong adaptability. However, the pattern period detection method based on the autocorrelation function can only reflect the periodic features of the pattern and has no other features. Moreover, it cannot efficiently acquire periodic information of a periodic pattern that has a sparsely distributed and large size in the image. Similarly, the measurement of the surface braiding angle and pitch length of the three-dimensional braided composite was realized by the corner detection-based method [10; 11; 12]. However, this algorithm based on corner detection is not suitable for the measurement of multi-period pattern parameters. When faced with complicated patterns, the corner points detected by the corner detection algorithm are disordered and the pseudo corner points are too many, and the center point of the pattern cannot be accurately found, so that the pattern period cannot be effectively extracted. In this paper, a new measurement method based on Faster region convolutional neural network (R-CNN) [13] was proposed to measure the parameters of the multi-period pattern. At present, Faster R-CNN has been applied in many fields, such as license plate detection [14], scene text detection [15] and optical image detection [16], and achieved excellent results with its powerful performance. As Faster R-CNN has the advantages of high object detection accuracy, fast speed and strong adaptability, we used Faster R-CNN as the pattern detector to locate the pattern and extract the pattern period. The contributions of this paper are as follows:  
* We have established the first multi-period pattern dataset. This dataset contains 5000 pattern images with size of \(512\times 640\) pixels and contains a total of six types of patterns. Moreover, each pattern in the image was annotated as an object in VOC 2007 format. * We have proposed to use the object detection network in deep learning to locate the pattern. We selected the training model of Faster R-CNN as the pattern detector and generated the bounding boxes enclosing the patterns to achieve the approximate positioning of the pattern. * We have proposed an automatic threshold method to extract the contour of the pattern and calculated the center points to obtain the precise positioning of the pattern. The four-neighbor-method was used to fill the missing center points to acquire the center point map that reflects the periodic characteristics of the pattern.