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Fabric Defect Detection Based on Improved Object As Point


Affiliations
1 Department of Mechanical Engineering, 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 real-time 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.

Keywords

Fabric Defects Detection, Object As Point, Data Augmentation, Deep Learning.
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  • Fabric Defect Detection Based on Improved Object As Point

Abstract Views: 259  |  PDF Views: 131

Authors

Yuan He
Department of Mechanical Engineering, National University of Singapore, Singapore
Xin-Yue Huang
Department of Mechanical Engineering, National University of Singapore, Singapore
Francis Eng Hock Tay
Department of Mechanical Engineering, National University of Singapore, Singapore

Abstract


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 real-time 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.

Keywords


Fabric Defects Detection, Object As Point, Data Augmentation, Deep Learning.

References