Multi-scale feature fusion network-based weft density analysis

Abstract The detection of weft density in fabrics is an important indicator for fabric quality control. Traditional methods for weft density analysis based on machine vision require the design according to fabric characteristics, lacking adaptability for different fabrics. To address this issue, a weft density detection method based on a multi-scale feature fusion network is proposed. First, a fabric image acquisition system with a transmissive light source is constructed to create a dataset with weft yarn contours. Second, the multi-scale feature fusion network (MSRUNeXt) is designed, which achieves precise segmentation and feature extraction of fabric weft textures through the collaborative cooperation of the multi-scale residual convolution module (MRCM), dual-channel upsampling module (DCUM), and global-local spatial attention module (GLSA). Finally, the progressive probabilistic Hough transform method is used to calculate the weft inclination angle and correct the fabric image, and the spatial positions of weft yarns are statistically analyzed by gray projection methods to realize precise calculation of weft density. Experimental results demonstrate that this method effectively detects weft density in different fabric types, exhibiting good adaptability and accuracy.