Automatic Fabric Defect Detection using Deep CNN-AlexNet Models

Defect detection stands as a pivotal quality control measure within the realm of manufacturing. Recent studies have witnessed the successful implementation of defect detection systems across diverse domains, ranging from scrutinizing steel surfaces to assessing fruit grades, and notably, integrating these systems into smart factories. In textile manufacturing, the automated identification of fabric defects has become indispensable, given the intricate nature of fabric textures, where minute imperfections can easily elude human perception. Consequently, relying solely on human inspection proves inadequate for resolving these challenges. Moreover, there has been a notable upswing of interest in applying IoT and machine learning techniques to bolster defect detection processes, owing to their demonstrated reliability. Consequently, the pursuit of defect detection has shifted towards a foundation rooted in Deep Learning. The core objective of this paper revolves around honing fabric defect detection through the utilization of CNN, a renowned deep learning model. This research utilizes CNN based pretrained models for fabric defeat prediction. It takes the popular AlexNet pretrained models for fabric defeat prediction on AITEX Fabric Image Set. The experimental results show that AlexNet provide a good accuracy 93.4% compared with normal CNN model.