Broken stitch detection method for sewing operation using CNN feature map and image-processing techniques

The inspection of sewing defects is an essential step in the quality assurance of garment manufacturing. Although traditional automated defect detection applications have shown good performance, these methods are usually configured with handcrafted features designed by a human operator. Recently, deep learning methods that include Convolutional Neural Networks (CNNs) have demonstrated excellent performance in a wide variety of computer-vision applications. To take advantage of the CNN's feature representation, the direct utilization of feature maps from the convolutional layers as universal feature descriptors has been studied. In this paper, we propose a sewing defect detection method using a CNN feature map extracted from the initial layers of a pre-trained VGG-16 to detect a broken stitch from a captured image of a sewing operation. To assess the effectiveness of the proposed method, experiments were conducted on a set of sewing images, including normal images, their synthetic defects, and rotated images. As a result, the proposed method detected true defects with 92.3% accuracy. Moreover, additional conditions for computing devices and deep learning libraries were investigated to reduce the computing time required for real-time computation. Using a general and cheap single-board computer with resizing the image and utilizing a lightweight deep learning library, the computing time was 0.22 s. The results confirm the feasibility of the proposed method's performance as an appropriate manufacturing technology for garment production.

Automated summary

This paper proposes a method for sewing defect detection using deep learning and a pre-trained VGG-16 model. Experimental results show that the proposed method achieves 92.3% accuracy in detecting real defects, and further investigation on reducing computation time for real-time performance is conducted.