Kahandawala, B, SNalmi, RSodige, B, A.KSubasinghage, KSilva, B, NWijesinghe, R,EWoo, S, T2026-08-152026-0925901230https://rda.sliit.lk/handle/123456789/5198Temporary stitches are essential in apparel manufacturing as they temporarily secure fabric pieces to prevent misalignment during machine sewing and ensure high-quality results. Manual ultrasonic tacking machines were introduced to enhance the precision of this process; however, the necessity for expert operators remains a major constraint. This work introduces a real-time system for ultrasonic tacking machines that combines vision-guided single-modal MUltichannel Feature Fusion Template Matching (MUFF-TM) to autonomously identify and align tacking points on textiles with sub-pixel spatial accuracy. To overcome the limitations of classic feature-based algorithms on smooth and deformable fabrics, the proposed method utilizes macro-contour extraction and equidistant boundary sampling rather than relying on unstable local textures. Experimental results demonstrate that MUFF-TM achieves a 100% target detection rate with a highly stable Mean Absolute Error (MAE) of under 5 pixels across various dynamic conditions, including changes in orientation, illumination, scale, and non-rigid deformation. By significantly outperforming traditional algorithms (SIFT, SURF, and ORB) in spatial precision, the developed software interface offers a robust, versatile, and scalable solution for advancing automated precision in the apparel industry. CopyrightenComputer visionGarment TemplateTacking automationTemplate matchingUltrasonic tackingArticleDOI: 10.1016/j.rineng.2026.111491