Volume 13, Number 3

Fabric Defect Detection based on Improved Object as Point


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


In the field of fabric manufacturing, many factories still utilise the traditional manual detection method. It requires a lot of labour, resulting in high error rates and low efficiency. In this paper, we represent a realtime automated detection method based on object as point. This work makes three attributions. First, we build a fabric defects database and augment the data to training the intelligence model. Second, we provide a real-time fabric defects detection algorithm, which have potential to be applied in manufacturing. Third, we figure out CenterNet with soft NMS will improved the performance in fabric defect detection area, which is considered an NMS-free algorithm. Experiment results indicated that our lightweight network based method can effectively and efficiently detect five different fabric defects.


Fabric defects detection, Object as point, data augmentation, Deep learning .