Phased Noise Enhanced Multiple Feature Discrimination Network for fabric defect detection

Abstract

Fabric defect detection is crucial for evaluating the quality of textile products. However, the subtlety and scarcity of fabric defects pose challenges to the task of detecting. Therefore, we propose a Phased Noise Enhanced Multiple Feature Discrimination Network, which is based on phased noise enhancement strategy and multiple feature discrimination module to improve the model’s ability to identify complex and subtle flaws. Specifically, we propose the phased noise enhancement strategy in the feature space to simulate feature-level anomalies that are closer to reality. This strategy can improve the input quality of the feature reconstructor, so that helps its perception and reconstruction ability. Then, we propose the multiple feature discrimination module, which has dual feature branches to improve its ability to distinguish more complex detailed texture features. In addition, we propose a subsampling module to reduce feature redundancy and ensure efficient inference speed. Finally, we conduct extensive experiments and ablation studies on two publicly available fabric datasets, AITEX and Kaggle Fabric. The experimental results show that the proposed method achieved 92% and 100% image level metrics and 97.5% and 67.1% pixel level metrics on two datasets, respectively, which is superior to the current state-of-the-art methods. In addition, our method also demonstrated significant performance in generalization experiments.