Abstract:

Objective Aiming at variety detection of cheese yarn on the transport guide rail in practical situations, this paper proposed an automatic solution based on image processing. Based on the characteristics of bobbin, a yarn variety detection method based on bobbin image classification was proposed to replace subjective judgment. The method proposed in this paper is aimed to reduce error rate of manual detection and labor costs, and to improve the production efficiency of spinning.
Method In order to facilitate feature extraction, the original image of cheese yarn was processed by the image segmentation method to obtain the bobbin area. Then, the segmented image of the annular bobbin was expanded into a square image by polar coordinate transformation. Color and texture features were extracted from the expanded image and optimized based on feature classification experiments and the elapsed time to jointly characterize the bobbin image.
Results The Otsu threshold method was adopted to find the gray threshold of the bobbin foreground and background, and a binary image was obtained based on the determined threshold. The image contour was used to filter non-target regions in the binary image by setting the area and perimeter threshold of the bobbin region. The binary image containing only the region of the bobbin was adopted as a mask to segment the yarn tube from the original cheese yarn image. For the segmented annular yarn tube, the polar coordinate transformation was applied to transform it into a rectangular image with the circumference of the outer circle and the width of the outer circle. Bobbin image expanded after threshold segmentation provided data support for subsequent research. The non-uniform quantized color histogram features with H:S:V=8:3:3 were optimized by the classification accuracy and the elapsed time of feature extraction (Tab.1). The features obtained by the preferred color quantization method demonstrated satisfactory classification effect on different types of bobbin images with the acceptable calculation time. The performance of different local binary pattern operators was optimized by the bobbin classification experiments. The operator of rotation invariant equivalent LBP_16,2 with the sampling point of 16 and the sampling radius of 2 was optimized to extract the texture features of the bobbin image (Tab.2). The experiments using the fusion feature were performed on bobbin with the same color, the same pattern, different colors and patterns and the results proved that the fusion feature is able to adapt to the bobbin classification detection task in the three cases (Tab.3). The classification results of different feature combinations under the same classification model and the performance of fusion features on different classifiers proved that the method of combining color and texture and support vector machine classification model is effective for bobbin detection (Tab.4, Tab.5).
Conclusion The combination of Otsu threshold method and image contour is successfully used to segment the bobbin from the original image, and it is shown that the bobbin image expanded by polar coordinate transformation can facilitate feature extraction. The optimal technique to characterize the color information of the bobbin image is proven to be the non-uniform quantization color histogram feature of H:S:V= 8:3:3. The preferred way to characterize the texture features of the bobbin image is the histogram feature of the rotation invariant equivalent LBP_16,2. The fusion of the two features can deal with the classification detection tasks of the bobbin in the same color, the same pattern, and different colors and patterns. Different classifiers were compared by the experiments and support vector machine was selected as the optimal classification model because of the best performance. The classification accuracy of this method is 100% in the same pattern of star pattern bobbin, black pattern bobbin and mixed pattern bobbin, which is the highest among different methods. The proposed method has practical value for variety detection of bobbin on the transport guide rail.

Key words: bobbin, support vector machine, polar coordinate transformation, feature extraction, image classification, variety detection