Defect Detection Techniques for Airbag Production Sewing Stages

TL;DR

This paper introduces an automated image processing framework for detecting sewing defects in airbag production, ensuring quality control by identifying issues like skipped or missed stitches early in manufacturing.

Contribution

The paper presents a novel framework that automatically detects and marks sewing defects in airbag manufacturing using image processing techniques.

Findings

Effective detection of skipped and missed stitches

Framework accurately follows seam paths and validates stitch patterns

Potential to improve quality control in airbag production

Abstract

Airbags are subject to strict quality control in order to ensure passengers safety. The quality of fabric and sewing thread influence the final product and therefore, sewing defects must be early and accurately detected, in order to remove the item from production. Airbag seams assembly can take various forms, using linear and circle primitives, with threads of different colors and length densities, creating lockstitch or double threads chainstitch. The paper presents a framework for the automatic detection of defects occurring during the airbag sewing stage. Types of defects as skipped stitch, missed stitch or superimposed seam for lockstitch and two threads chainstitch are detected and marked. Using image processing methods, the proposed framework follows the seams path and determines if a color pattern of the considered stitches is valid.