Broken stitch detection system for industrial sewing machines using HSV color space and image processing techniques

Sewing defect detection is an essential step in garment production quality control. Although sewing defects significantly influence the quality of clothing, they are yet to be studied widely compared to fabric defects. In this study, to address sewing defect detection and develop an appropriate method for small and labor-intensive garment companies, an on-machine broken stitch detection system is proposed. In hardware, a versatile mounting kit, including clamping, display, and adjustable linkage for a camera, is presented for easy installation on a typical industrial sewing machine and for placing the camera close to the sewing position. Additionally, a prototype is implemented using a low-cost single-board computer, Raspberry Pi 4 B, its camera, and Python language. For automated broken stitch detection, a method is proposed that includes removing the texture of the background fabric, image processing in the HSV color space, and edge detection for robust broken detection under various fabric and thread colors and lighting conditions. The proposed system demonstrates reasonable real-time detection accuracy. The maximum accuracy obtained on a sewing stitch dataset with 880 images and on-site tests of various industrial sewing machines is 82.5%, which is 12.1–34.6% higher than that of the two existing methods.