Automated Sewing System Enabled by Machine Vision for Smart Garment Manufacturing

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Abstract
This letter presents an automated sewing system designed for smart garment manufacturing, incorporating machine vision capabilities into a custom-built sewing machine. The vision system captures an image of the fabric pattern placed between two acrylic plates with a small opening, utilizing a deep learning model to detect and segment the opening, which represents the area of interest on the plate. Subsequently, a specialized algorithm detects a narrow seam line within the segmented image and generates a stitching path alongside the seam line, ensuring a consistent distance. The sewing machine then accurately stitches along the generated path automatically. The vision system utilized in this study achieves a spatial resolution of 68 $\mu$ m per pixel. The custom-built sewing machine, controlled by an external computer, exhibits a spatial resolution of 10 $\mu$ m, a translation speed of 60 mm/s, and an adjustable stitching interval ranging from 1 mm to 5 mm. The subsystems and components are interconnected using the Robot Operating System (ROS), enabling seamless communication and integration. The proposed system eliminates the need for human intervention, facilitating automated garment production. This innovative system is expected to play a critical role in realizing the vision of smart garment manufacturing.
Funding Information
  • SNU-Hojeon Garment Smart Factory Research Center
  • Hojeon Ltd.
  • National Research Foundation (RS-2023-00208052)

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