Abstract:
Identifying sewing machine types based on stitches plays a crucial role in
ensuring the quality of garment production. For buying companies, to order
garment products, it‟s a requirement to check the quality of the product.
Deciding the specific quality of the product manually is a difficult task.
Therefore, in the textile industry, there is a necessity for an automated
system that can easily identify and verify whether the sewing machine type is
correct or not, so the orders can be accepted or rejected easily. The model
proposed in this paper helps to detect the type of sewing machine based on
the stitches on clothing. This research is significant because it improves the
quality control of garment production and ensures that buying companies can
verify that orders meet the required standards. With the proposed model not
only large-scale garments but small-scale garments can also identify the
sewing machine types and solve their day-to-day problems effectively. The
main objectives of the research are to create a data set of stitch images,
segment the stitch patterns from images, extract features from the segmented
images, and train the model to identify the sewing machine types. In this
proposed methodology, the Keras Sequential Model has been employed to
explore the automated identification of various sewing machine types
through their unique stitch patterns. The research methodology involved
enhancing image quality by reducing noise, selecting appropriate tools for
system modeling, training the model with machine learning algorithms, and
comparing the results with manual checks. The model achieved an accuracy
of 82.80%. Identifying the defects in fabric during the manufacturing process
and automatically categorizing or matching textile colors to improve dyeing
and manufacturing processes are significant impacts that can be achieved in
the future using the Keras Sequential Model.
