Weft Skew Detection in Woven Fabrics Based on Rotation-Based Projection Guidance and Adaptive Frequency-Domain Reconstruction

Conventional weft skew detection methods are often sensitive to edge quality and parameter settings and may perform inconsistently across fabrics with different weave structures, densities, local texture disturbances, and interlaced warp–weft textures. This study proposes a coarse-to-fine method combining rotation-based projection guidance with adaptive frequency-domain reconstruction. Rotation-based projection profiling first provides a coarse-angle constraint. The dominant-frequency radius and dominant spectral orientation are then determined adaptively to construct a frequency-domain mask for weft-yarn texture reconstruction. Candidate weft-yarn line segments are extracted by local adaptive thresholding, skeleton extraction, line fitting, and screening. The final angle is obtained using a length-weighted circular mean and median absolute deviation (MAD)-based outlier rejection. The method was evaluated on 164 distinct fabric samples comprising 1804 test images. It achieved a mean absolute error (MAE) of 0.09°, a root mean square error (RMSE) of 0.12°, a maximum absolute error of 0.48°, and an average runtime of 104.36 ms. Compared with rotation-based projection profiling, the Hough transform, and a baseline method combining the fast Fourier transform (FFT) with the Hough transform, the proposed method yielded lower errors and a more concentrated error distribution, supporting its effectiveness for automated weft skew detection in woven fabrics.