Automated Top Stitching via Vision-Based Macro-Mini Approach: Retrofitting Legacy Machines for Enhanced Precision in Garment Manufacturing

The apparel industry is increasingly adopting automation technologies and investing in research to address global reshoring efforts and rising labor costs. One essential process for automation in garment factories is top stitch, which involves sewing two fabric sheets together along a seam line while maintaining a consistent distance from it. This paper presents an automated top stitch system that retrofits a legacy machine using a vision-based macro-mini approach. The system mainly comprises a two-degree-of-freedom automatic sewing machine as the legacy machine, a vision sensor module, a mini-actuator module, and an infrared sensor module. The vision sensor module detects the seam line on the fabric, even in the presence of positional uncertainties, such as, human errors. The infrared sensor module monitors the sewing sequence, while the mini-actuator module operates simultaneously with the sewing machine to compensate for any misalignment. The system demonstrates improvements, with the average and the maximum errors reduced by 79% and 67%, respectively, and the standard deviation improved by 73%, compared to those from the operation without our system. To the best of our knowledge, this is the first development of an autonomous top stitch system using a retrofitted legacy machine, presenting a novel architecture that integrates sensors and actuators through a macro-mini approach. Note to Practitioners—This paper implements key technologies essential for automating top stitch. Notably, since conventional vision recognition methods struggle to distinguish a seam line, an asymmetric lighting environment and a specialized algorithm were developed to address the challenge. Additionally, the system architecture was designed with consideration for upgrading existing legacy machines, enabling seamless integration without requiring full equipment replacement. While this paper focuses on the practical challenge of top stitch automation, the proposed architecture can be extended beyond sewing applications. It can be applied to other precision-driven processes that rely on vision-based object differentiation, such as welding or dispensing. This approach is particularly beneficial for enterprises hesitant to adopt new equipment without prior validation, offering a cost-effective alternative to full system replacements. Given the expenses associated with dismantling, disposing of, and replacing existing machinery, introducing modular upgrades provides a practical means of verify process improvements while minimizing expenses. Although the design and performance of additional modules may vary depending on the legacy machine being retrofitted, substantial improvements can still be achieved. Experimental results demonstrate that this approach reduces the average error by 79% and enhances the standard deviation by 73%, validating its feasibility from both engineering and economic perspectives.