Volume 12, Number 4

Fabric Defect Detection based on Improved Faster RCNN


Yuan He, Han-Dong Zhang, Xin-Yue Huang and Francis Eng Hock Tay, National University of Singapore, Singapore


In the production process of fabric, defect detection plays an important role in the control of product quality. Consider that traditional manual fabric defect detection method are time-consuming and inaccuracy, utilizing computer vision technology to automatically detect fabric defects can better fulfill the manufacture requirement. In this project, we improved Faster RCNN with convolutional block attention module (CBAM) to detect fabric defects. Attention module is introduced from graph neural network, it can infer the attention map from the intermediate feature map and multiply the attention map to adaptively refine the feature. This method improve the performance of classification and detection without increase the computation-consuming. The experiment results show that Faster RCNN with attention module can efficient improve the classification accuracy.


Fabric defects detection, Faster RCNN, Convolutional block attention module, Deep learning.