Enhancing Textile Quality Assurance with TensorFlow: Detecting Fabric Anomalies

The detection of fabric defects is an important task in the textile industry, as it can help to identify and remove defective fabrics before they are sold to customers. Machine learning and computer vision methods have been applied to fabric defect identification with encouraging outcomes in recent years. There are few drawbacks in traditional system such as slow speed and limited accuracy in to order to overcome the drawbacks this paper proposes a Detection of Fabric Anomalies Using TensorFlow. The Proposed method uses a pre-trained model as a starting point and fine-tunes it on a dataset of fabric images. The dataset includes both normal and defective fabrics, and the defects are labeled to train the model to recognize them. The performance of the proposed model is evaluated using metrics such as the average precision (mAP). The results show that the proposed method is more effective in the real-time detection of fabric defects compared to conventional methods. The proposed method is scalable and can be applied to different types of fabrics.