Enhancing fabric quality control: Implementing real-time defect detection with image processing techniques and arduino

Abstract

The textile industry is a rapidly growing sector globally and plays a momentous role in many sectors like manufacturing, employment, and business operations in many developed countries. Cloth flaws account for over 85% of the failures experienced in the garment industry. Efforts are currently underway to enhance fabric consistency, making the identification of defects a critical step in the textile manufacturing process. However, the traditional manual inspection technique for detecting cloth flaws is time-consuming and labor-consuming. Consequently, automation has been introduced through image processing technology. This approach utilizes image processing techniques in MATLAB to locate faults, with fault detection carried out using an Arduino. To improve the accuracy of fabric defect identification, an electronic fabric inspection method has been proposed. This framework incorporates image processing techniques, employing MATLAB, and real-time applications implemented on the Arduino kit. Neural Networks serve as the optimal classifiers for fault classification. Upon detecting a flaw in the fabric, the system breaks shortly to remove the defective component. The identified fault is then displayed on the LCD, and the buzzer is activated.