Abstract:
The adoption of image processing-based technologies in the
textile sector is rising. This technology is commonly utilized to
replace traditional sensor systems that are limited to a single
function while also improving product quality control functions.
Defects during the manufacturing process are a common problem
in the textile business, particularly with fabric products. This
study created a fabric quality control system that detects fabric
problems using machine learning-based picture classification
techniques. A D320p web camera detects rare and slap flaws,
which are classified using open-source Google teaching machine
software and processed on a Raspberry Pi 3B device. The
laboratory-scale measurement was carried out on a prototype
cloth rolling machine using the confusion matrix method. The test
results reveal an average inference speed of 143.5 milliseconds, a
frame rate of 6.45 fps, and a 98.56% accuracy rate. These results
demonstrate that the proposed system is effective and efficient for
detecting fabric defects, offering a promising solution for
enhancing quality control in the textile industry. Future research
could focus on scaling the system for industrial use and enhancing
real-time performance.
