Open AccessFabric Defect Detection based on Improved Object as Point
Author(s) -
Yuan He,
Xin-Yue Huang,
Francis Eng Hock Tay
Publication year - 2021
Publication title -
international journal of computer science and information technology/international journal of computer science and information technology (chennai. print)
Language(s) - English
Resource type - Journals
eISSN - 0975-4660
pISSN - 0975-3826
DOI - 10.5121/ijcsit.2021.13301
Subject(s) - computer science , artificial intelligence , point (geometry) , field (mathematics) , object detection , object (grammar) , computer vision , pattern recognition (psychology) , real time computing , geometry , mathematics , pure mathematics
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.